NAIST-IS-DT0361008

Doctor’s Thesis

Constructing, Refining and Exploiting
Rich Linguistic Resources

Sanae Fujita (Kawai)

March 24, 2009

Department of Information Processing
Graduate School of Information Science
Nara Institute of Science and Technology

Doctor’s Thesis
submitted to Graduate School of Information Science,
Nara Institute of Science and Technology
in partial fulfillment of the requirements for the degree of
DOCTOR of ENGINEERING

Sanae Fujita (Kawai)
Thesis committee: Yuji Matsumoto, Professor
Kiyohiro Shikano, Professor
Kentaro Inui, Associate Professor
Francis Bond, Doctor

Constructing, Refining and Exploiting
Rich Linguistic Resources∗
Sanae Fujita (Kawai)

Abstract
Linguistic resources, such as corpora, thesauruses, and (machine readable) dictionaries, are important as training data and knowledge sources in Natural Language
Processing (NLP). These resources can take various forms. For example, corpora canbe annotated with a variety of information; part-of-speech tags, syntax trees, and word
sense information; to name a few, or none at all in the case of raw corpora.
Recently, the target of natural language processing becomes deeper and deeper,
shifting from surface to sense, from morphological analysis to syntactic analysis, thento semantic analysis. Therefore, importance of linguistic resources with rich syntactic
and semantic information increases.
There are several methods to construct resources, for example, hand-construction,
automatic-construction, and semi-automatic-construction. With the increasing the amount of machine-readable data, automatically-constructed resources have become more
popular. Generally, automatically-constructed resources are easy to expand and havehigh topicality, but unfortunately, they are relatively shallowly analyzed and include
errors. Moreover, the richer resources we try to construct, the more difficult automaticconstruction becomes. On the other hand, hand-construction is both time consuming
and costly, but can provide much richer resources.
In this thesis, we focus on such rich resources, and describe the methods of constructing, refining and exploiting them. First, we describe the background of our research, and the thesis outline in Chapter 1. Then, in Chapter 2, we introduce the resources related with our research; the Japanese Ontology Goi-Taikei, bilingual valency∗ Doctor’s Thesis, Department of Information Processing, Graduate School of Information Science,

Nara Institute of Science and Technology, NAIST-IS-DT0361008, March 24, 2009.

i

(pattern) dictionary, the Hinoki Corpus, and the Lexeed Dictionary. These resources
are related to each other, and have been constructed by hand. Then, we propose
some methods to extend them effectively (Chapter 3 and 4), and prove their usefulness through several task-based evaluations (Chapter 5 and 6). Finally, in Chapter 7,
we reconfirm the importance of studies on constructing, refining and exploiting richlinguistic resources.

Keywords:
natural language processing, valency dictionary, ontology, thesaurus, treebank, sensebank, alternation, parse ranking, word sense disambiguation, Japanese, English, HPSG

ii

Acknowledgements
I am deeply grateful to Professor Yuji Matumoto of Nara Institute of Science and Technology for his patient support and supervising this thesis.
I am also deeply grateful to Dr. Francis Bond (Present affiliation: National Instituteof Information and Communications Technology (NiCT)), who supervised me at NTT
Communication Science Laboratory, and gave me valuable, constructive and fruitfulsuggestions and comments.
I am also grateful to Associate Professor Kentaro Inui and Professor Kiyohiro
Shikano of Nara Institute of Science and Technology for helpful comments.
I wish to express my gratitude to NTT Communication Laboratory that gave me achance to take a doctor’s degree.
I would like to thank the other members of the NTT Natural Language ResearchGroup, and it’s predecessor Machine Translation Research Group for their supports.
Especially Takaaki Tanaka (Present affiliation: NTT West Corporation), Dr. Hiromi
Nakaiwa and Dr. Satoshi Shirai (Present affiliation: NTT Advanced Technology Corporation (NTT-AT)).
A part of this research was supported by the research collaborations between the
NTT Communication Science Laboratory and CSLI, Kyoto University, or Melborune
University. I would like to thank the members of these laboratories, especially Dr.
Timothy Baldwin and Dr. Shigeko Nariyama, for helpful comments.
I would also like to thank Eric Nichols and Dr. Franklin Chang for their volunteer
correction for my English.
Then, I would like to express my gratitude to people who evaluated translations,annotated corpora, refined resources, and so on. Especially Takayuki Kuribayashi,
Tomoko Tanaka (Hirata) and members of NTT-AT and IR-ALT.
Finally, I wish to thank my family, my partner Takuma Kawai for his understandiii

ing, my children Azumi, Ryuto and Karin for their encouraging smiles, and my parents,
Toru and Miyoko Fujita, for their hearty encouragements.

iv

Contents
Abstracti

Acknowledgements

iii

1 Introduction
1.1 The Importance of Linguistic Resources . . . . . . . . . . . . . . . .
1.2 Aims of This Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Methods of Constructing Linguistic Resources . . . . . . . . . . . . .
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1
1
1
2
3
5

2 Resources
2.1 Japanese Ontology: Goi-Taikei . . . . . . . . . . . . . . . . . . . . .
2.1.1 Comparison with Other Resources . . . . . . . . . . . . . . .
2.2 ALT-J/E’s Valency (Semantic Pattern) Dictionary . . . . . . . . . . .
2.2.1 Comparison with Other Resources . . . . . . . . . . . . . . .
2.3 Japanese Semantic Database: Lexeed . . . . . . . . . . . . . . . . . .
2.3.1 Comparison with Other Resources . . . . . . . . . . . . . . .
2.4 Japanese Treebank / Sensebank: Hinoki . . . . . . . . . . . . . . . .
2.4.1 Syntactic Annotation . . . . . . . . . . . . . . . . . . . . . .
2.4.2 Semantic Annotation . . . . . . . . . . . . . . . . . . . . . .
2.4.3 Comparison with Other Resources . . . . . . . . . . . . . . .

7
7
10
14
17
18
24
27
27
30
32

3 Extending the Coverage of a Valency Dictionary
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 The Number of Valency Patterns Required . . . . . . . . . . .

35
35
37

v

3.1.2 Coverage of Original Valency Patterns . . . . . . . . . . . . .
3.1.3 Utility of Valency Information . . . . . . . . . . . . . . . . .
Method of Creating Patterns . . . . . . . . . . . . . . . . . . . . . .
3.2.1 Overview of Method of Creating Patterns . . . . . . . . . . .
3.2.2 Constructing Candidates . . . . . . . . . . . . . . . . . . . .
3.2.3 Filtering Candidates . . . . . . . . . . . . . . . . . . . . . .
3.2.4 Making Candidates Robust . . . . . . . . . . . . . . . . . . .
Creation and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.1 Target Verbs . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.2 Results of Creation using Several Filtering Methods . . . . .
3.3.3 Translation Task-based Evaluation of Filtering Methods . . .
3.3.4 Lexicographers’ Evaluation of Filtering Methods . . . . . . .
3.3.5 Evaluation of Alternations and Merging . . . . . . . . . . . .
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 Analysis of the Translation Results . . . . . . . . . . . . . .
3.4.2 Analysis of the Lexicographers Evaluation . . . . . . . . . .
3.4.3 Refining the Method . . . . . . . . . . . . . . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38
39
43
43
44
44
51
53
53
54
54
57
59
60
61
62
63
68

4 Acquisition of Valency Entries using Alternation Data
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Alternations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Comparing Selectional Restrictions of A, O and S . . . . . . . . . . .
4.4 Method of Creating Valency Entries . . . . . . . . . . . . . . . . . .
4.4.1 Target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.2 Creating the Japanese subcat and SRs . . . . . . . . . . . . .
4.4.3 Creating English Side . . . . . . . . . . . . . . . . . . . . .
4.4.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.5 Evaluation: Entry Possible/Impossible . . . . . . . . . . . . .
4.4.6 Evaluation: Fine Tuning . . . . . . . . . . . . . . . . . . . .
4.4.7 Japanese Side . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.8 English Side . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1 Rejecting Impossible Candidates . . . . . . . . . . . . . . . .

69
69
70
72
74
74
76
76
78
78
79
79
81
81
81

3.2

3.3

3.4

3.5

vi

4.5.2 Improving the English Translations . . . . . . . . . . . . . .
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

82
84
84

5 Exploiting Semantic Information for HPSG Parse Selection
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Parse Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.1 Syntactic Features . . . . . . . . . . . . . . . . . . . . . . .
5.2.2 Semantic Features . . . . . . . . . . . . . . . . . . . . . . .
5.3 Evaluation and Results . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.1 A Maximum Entropy Ranker . . . . . . . . . . . . . . . . . .
5.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

85
85
86
86
88
92
93
93
96
96

4.6
4.7

6 Word Sense Disambiguation using Disambiguated Superordinate Semantic Classes
99
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.2 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.3 Superordinate Semantic Class Disambiguation . . . . . . . . . . . . . 102
6.3.1 Mapping Word Sense to Superordinate Semantic Class . . . . 102
6.3.2 Problems in Mappings . . . . . . . . . . . . . . . . . . . . . 103
6.3.3 Data used . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.3.4 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.3.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . 107
6.4 Word Sense Disambiguation (WSD) . . . . . . . . . . . . . . . . . . 109
6.4.1 Comparison with SENSEVAL-2 Japanese Task . . . . . . . . 109
6.4.2 Effect on Unseen Words . . . . . . . . . . . . . . . . . . . . 112
6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.6 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
7 Conclusion
115
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
vii

7.2
7.3

Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

A Data on the distribution of Goi-Taikei’s Semantic Classes

121

B Classification of English Alternations for Japanese S = O Alternation

129

Bibliography

148

List of Publications

159

viii

List of Figures
1.1

Roles of Linguistic Resources in Natural Language Processing . . . .

2

Overview of our Resources and Aims . . . . . . . . . . . . . . . . .
Top four levels (Lvl 0-3) of the Goi-Taikei Common Noun Ontology .
shirei-suru ⇔Valency (Semantic Pattern) Entry for the verb
order No.1 (SVOP) . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Valency (Semantic Pattern) Entry for the verb
shirei-suru ⇔order No.2 (SVPC) . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5 Overview of links between the Linguistic Resources . . . . . . . . . .
doraibâ “driver” from Lexeed (with
2.6 Entry for the Word
English glosses) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
untenshu “chauffeur” from Lexeed (with
2.7 Entry for the Word
English glosses) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8 Search Interface for Lexeed:
doraibā “driver” . . . . . . .
2.9 Syntactic View of the Definition of
1 untenshu “chauffeur” . . .
2.10 Derivation Tree of the Definition of
1 untenshu “chauffeur” . .
2.11 Simplified Dependency View (MRS) of the Definition of
1 untenshu “chauffeur” . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.12 Interpretation for MRS of
1 untenshu “chauffeur” (Figure 2.11)

8
9

2.1
2.2
2.3

Æ2d
Æ2d

À{§

þU3

À{§
þU3
þU3

þU3

þU3

3.1
3.2
3.3
3.4

Point of the Idea for Extending the Coverage of a Valency Dictionary .
Graph of Cover Ratio for Japanese Newspapers (9 years) . . . . . . .
Valency (Semantic Pattern) Entry for the verb
joushin-suru⇔ report No.1 (SVOP) . . . . . . . . . . . . . . . . . . . . . . . . .
joushin-suru
Valency (Semantic Pattern) Entry for the verb
⇔ report No.2 (SVPC) . . . . . . . . . . . . . . . . . . . . . . . . .

ê 2d
ê 2d

ix

15
16
20
21
22
23
29
29
31
31
36
39
41
42

3.5
3.6
3.7
3.8
3.9

Flow of Creating New Patterns . . . . . . . . . . . . . . . . . . . . .
Creating Candidates through a Common Pivot Translation . . . . . .
Flow of Paraphrasing Check . . . . . . . . . . . . . . . . . . . . . .
Creating Candidates through Multiple Pivots . . . . . . . . . . . . . .
Graph of Cover Ratio of Created Patterns for Japanese Newspapers (9years) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.10 Candidate Pattern forsuppanuku “expose” (1) . . . . . . .
3.11 Candidate Pattern forsuppanuku “expose” (2) . . . . . . .

2<Je(
2<Je(

4.1
4.2
4.3
4.4
5.1
5.2

The Level of Semantic Classes . . . . . . . . . . . . . . . . . . . . .
toku “disExisting Entries (which undergo the S = O alternation):solve” ⇔tokeru “dissolve” . . . . . . . . . . . . . . . . . . .
odoroku “be surprised” ⇒ New entry
odorokasu
Seed:“surprise” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Method of Creating the English Side . . . . . . . . . . . . . . . . . .

V*d
Ø(

45
46
47
50
64
67
67

V(

73

Ø$2

75

þU2dunten-suru “N1 drive N2”. . . . . . . . . . . . . . . . . . .
Learning Curves (Definitions) . . . . . . . . . . . . . . . . . . . . .

75
77
91
95

À{

6.1
6.2

Entry for1 raitâ “lighter” from Lexeed . . . . . . . . . . . . 101
Simplified Example of Input Information and (Ideal) Lattice of Possible Superordinate Semantic Classes (Level 3) . . . . . . . . . . . . . 106

7.1

Plan to Expand Resources: from closed world to open domain, fromhand-build to semi-automatic . . . . . . . . . . . . . . . . . . . . . . 117xList of Tables
2.1
2.2
2.3

Number of Classes at each Levels of Goi-Taikei . . . . . . . . . . . .
Comparison of Goi-Taikei and Other Thesauruses/Ontologies: Size . .
Comparison of Goi-Taikei and Other Thesauruses/Ontologies: Classand Target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Comparison Lexeed and Other Resources . . . . . . . . . . . . . . .
2.5 Hand-Classification of Link types of Lexeed and Iwanami . . . . . . .
2.6 Number of Words Existing in both Lexeed and JUMAN . . . . . . . .
2.7 Number of Words Existing in Only One Dictionary (Lexeed or JUMAN)
2.8 Size of Hinoki’s Target Corpus . . . . . . . . . . . . . . . . . . . . .
2.9 Size of Hinoki Sensebank . . . . . . . . . . . . . . . . . . . . . . . .
2.10 Simplified Example of semantic annotated Hinoki corpus . . . . . . .
3.1
3.2
3.3

10
12
12
24
25
26
26
28
32
34

Cover Ratio for Japanese Newspapers (9 years) . . . . . . . . . . . .
Size of J-X Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . .
The Possibility of Increasing Cover Ratio for Japanese Newspapers (9years) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Number of Created Valency Patterns . . . . . . . . . . . . . . . . . .
3.5 Task-based Evaluation of New Patterns for each Filtering method . . .
3.6 Lexicographers’ Evaluation of New Patterns for each Filtering method
3.7 Number of Merged Patterns . . . . . . . . . . . . . . . . . . . . . . .
3.8 Lexicographers’ Evaluations for New Patterns . . . . . . . . . . . . .
3.9 Cover Ratio of Created Patterns for Japanese Newspapers (9 years) . .
3.10 Number of Creatable Valency Patterns Using Multilingual Check . . .

54
55
56
58
59
60
64
65

4.1
4.2

71
78

Classification of English Alternation . . . . . . . . . . . . . . . . . .
Is the Japanese Expression possible? . . . . . . . . . . . . . . . . . .
xi

38
50

4.3
4.4
4.5
4.6

Japanese Evaluation (Fine Tuning) . . . . . . . . . . . . . . . . . . .
Analysis of Corrected SR Vt . . . . . . . . . . . . . . . . . . . . . .
English Evaluation (Fine Tuning) . . . . . . . . . . . . . . . . . . . .
A Comparison of Reference Data with Created Alternations . . . . .

5.1

Example structural features (SYN-1 and SYN-GP) extracted from thederivation tree in Figure 2.10 . . . . . . . . . . . . . . . . . . . . . .
Example semantic features (SEM-Dep) extracted from the dependency
tree in Figure 2.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example semantic class features (SEM-Class). . . . . . . . . . . . . .
Example semantic features (SP) . . . . . . . . . . . . . . . . . . . .
Data of Sets for Evaluation . . . . . . . . . . . . . . . . . . . . . . .
Parse Selection Results . . . . . . . . . . . . . . . . . . . . . . . . .

5.2
5.3
5.4
5.5
5.6
6.1
6.2
6.3
6.4
6.5
6.6
6.7

Number of word senses per semantic class (at each level) . . . . . . .
Number of Semantic Classes per word sense . . . . . . . . . . . . . .
Data Sets for Superordinate Classes (All Words) . . . . . . . . . . . .
Results of Superordinate Semantic Class Disambiguation . . . . . . .
Data Sets for WSD (Senseval 100 words) . . . . . . . . . . . . . . .
Results of WSD (by SVM) . . . . . . . . . . . . . . . . . . . . . . .
Accuracy for words which didn’t appear in training data (Zero Frequency) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

80
80
80
82

87
89
90
92
93
94
102
104
105
108
111
111
112

A.1 Most Frequent 30 Semantic Classes in Japanese Dictionary . . . . . . 122
A.2 Top 30 Semantic Classes over Newspaper Text (first half of Kyoto Corpus): Noun Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
A.3 Distribution of Semantic Classes in Newspaper Text (The first half ofKyoto Corpus): Merged into Superordinate Semantic Classes at Level
2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
A.4 Distribution of Semantic Classes in Newspaper Text (The first half ofKyoto Corpus): Marged into Superordinate Semantic Classes at Level
3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
A.5 Samples of Semantic Classes which don’t appear in Newspaper Text
(The first half of Kyoto Corpus): Top 30 classes in Goi-Taikei’s Japanese
Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
xii

Chapter 1
Introduction
1.1 The Importance of Linguistic Resources
Linguistic resources, such as corpora, thesauruses, and (machine readable) dictionaries, are important as training data and knowledge sources in Natural Language Processing (NLP). Almost all NLP tools and applications use at least one or more resources.
For example, morphological analyzers (part-of-speech taggers) typically use lexicons
and have been improved using tagged corpora as training and test data.
Such linguistic resources are important and indispensable in every field of NLP.
Figure 1.1 shows a simplified illustration of the roles of linguistic resources in NLP.
Linguistic resources are the foundation, and all tools and applications (such as information retrieval, machine translation, and question answering) depend on these resources.
Therefore, linguistic resources serve as an important part of all NLP tools and applications. For this reason, it is important to efficiently and accurately construct, refine andexploit linguistic resources.

1.2 Aims of This Thesis
The ultimate aim of our research is to make machines capable of understanding natural
language (or make it behave as if it understood).
Most recent research is based on statistical models and/or machine learning methods. Generally, statistical methods are stronger for in-domain data, especially for fre1

Figure 1.1: Roles of Linguistic Resources in Natural Language Processing

quent sentences, words, word senses, and so on. But they are relatively weak forout-of-domain-data, especially for infrequent sentences, words or word senses. Because language is infinitely creative and variable, we still need to handle semantics for
infrequent data.
The rich resources needed to handle semantics are hard to construct. So the aimsof this thesis are to construct and provide such rich resources, to prepare a frameworkfor facilitating the construction of further rich resources. Furthermore, we also aim toshow the effectivity of semantics in some NLP tasks.

1.3 Methods of Constructing Linguistic Resources
As described above, Linguistic resources, such as corpora, thesauruses, and (machinereadable) dictionaries, can take various forms. For example, corpora can be annotatedwith a variety of information; part-of-speech tags, syntax trees, and word sense information; to name a few, or none at all in the case of raw corpora. Machine readable2

dictionaries can encode many kinds of information about a lexical entry; monolingual
definitions, foreign language translations, syntactic categories, case frame information,
or word sense information to name a few. Then, there are several thesauruses (ontologies) constructed from several different points of view.
We also classify these resources based on methods for construction: that is, handconstructed resources, automatically-constructed resources, and semi-automaticallyconstructed resources.
Recently, with the increasing amount of digital data, automatically-constructed resources have become more prevalent. For example, Japanese n-gram data (Kudo andKazawa, 2007) and Case Frames (Kawahara and Kurohashi, 2006) are automaticallyconstructed from an enormous amount of automatically collected web data. Generally, automatically-constructed resources are easy to expand and have high topicality,
but unfortunately, they are relatively shallowly analyzed and tend to be noisy (they include errors). Moreover, the richer the resources we try to construct, the more difficult
automatic-construction becomes.
On the other hand, while methods of hand-construction are both time consuming
and costly, they can provide more complex and reliable resources. Especially, to treat
meaning, we still need to construct rich resources such as sense tagged corpora, thesauruses, either by hand or semi-automatically.
Semi-automatic methods combine the advantages of hand-construction and automatic-construction. That is, for example, we can extend resources efficiently using hand-constructed rich resources to bootstrap the process. Or, we can manuallycorrect errors in resources which were constructed automatically at first. Generally,semi-automatic methods can provide more complex and reliable resources than fullyautomatic methods at lower cost and in less time than hand-construction.
In this thesis, we show how to initially construct rich resources by hand, then expand them semi-automatically.

1.4 Contributions
In this thesis, we construct valuable and unprecedented rich resources. Our aim is forthese resources to help make a breakthrough in NLP possible.
The resources we have constructed are already in use in various tasks. For example,3

dictionary Lexeed and corpus Hinoki are used to evaluate several methods of word
sense disambiguation; in specific, Lesk based method (Baldwin et al., 2008), a method
using both syntactic and semantic features (Tanaka et al., 2007), and a method using
superordinate semantic classes (See Chapter 6). The Lexeed dictionary also provides abasis for constructing verb dictionaries based on Lexical Conceptual Structure (LCS)by Takeuchi (2004).
We also construct the Hinoki treebank based on successful methods from the DELPHIN Project (Deep Linguistic Processing with HPSG)1 , which builds and provides ashared format, tools and rigid scheme of evaluation for many different languages (currently including English, Japanese, French, Norwegian, Spainish, and so on). Thus
there are several language resources (treebanks and grammars for parsing) which havethe same format as the Hinoki treebank; therefore the Hinoki treebank has high interavailability with multiple languages.
In this thesis, although we construct Japanese (and bilingual Japanese and English)
resources, the proposed methods are general and not tied to any particular language
pair or resources. For example, the method to expand bilingual valency patterns (inChapter 3) inspired Hong et al. (2004) to use the same method to expand KoreanChinese patterns. This shows that our method works for different systems and for
different language pairs.
Until recently, the effectiveness of semantic information was under question. However, we showed that semantic information (in particular, superordinate semantic classes)
works well for parse selection. It is especially effect when the training data size is relatively small. We thus expect that semantic information will make adaptation to new
domains and language easy. This proposed method also can be expanded to other languages using WordNet, EuroWordNet, and other similar resources. (See e.g., Agirre
et al. (2008)).
We also propose an easy-to-use method to estimate superordinate semantic classes.
We hope that this method will provide a basic tool for estimation of superordinate
semantic classes, and will be used for not only parse selection but also other NLP tasks
such as Semantic Role Labeling (SRL).

1 http://www.delph-in.net/

4

1.5 Thesis Outline
As described above, we believe that resources with rich information are important anduseful even for statistical natural language processing. Therefore, we have been constructing various resources, such as an ontology, valency (pattern) dictionary, treebank
and sensebank. In this thesis, first we introduce the features of these resources, then
we investigate the usage and effectiveness of these resources.
In the following chapters, we introduce the resources related to our research (Chapter 2). Then, we propose some methods to extend them effectively (Chapter 3, 4), andprove their usefulness through several task-based evaluations (Chapter 5, 6).
In more detail, in Chapter 2, we introduce the resources we will use later on; theJapanese Ontology Goi-Taikei and its bilingual valency (pattern) dictionary, the Hinoki
Treebank and Sensebank, and the Lexeed Dictionary. We also compare these resources
with other resources.
In Chapter 3, we present a method of extending the coverage of the bilingual valency (pattern) dictionary, by assigning valency information and selectional restrictions
to entries in a bilingual dictionary. The method exploits existing bilingual valency dictionaries and is based on two basic assumptions: words with similar meaning have
similar subcategorization frames and selectional restrictions; and words with the sametranslations have similar meanings. In this chapter, we evaluate our methods throughtranslation based evaluation and hand-evaluation.
In Chapter 4, first, we investigate the alternation features, then we present a methodthat uses alternation data to add new entries to an existing bilingual valency dictionary based on the features. We automatically created new valency entries using thecausative/inchoative alternation data. If the existing lexicon has only one half of thealternation, then our method constructs the other half of the alternation. The createdentries were hand evaluated.
In Chapter 5, we investigate the effectiveness of rich information by applying it toparse selection (ranking). In this chapter, we show that sense-based semantic features
combined with ontological information are effective for parse selection.
In Chapter 6, to get the sense information automatically, we propose a method forword sense disambiguation (WSD) using superordinate semantic classes. We separate
this method into two stages. At the first stage, we estimate superordinate semantic
classes, then at the second stage we estimate word senses using the results of the first5

stage.
Finally, we conclude with a summary and discussion of future work in Chapter 7.

6

Chapter 2
Resources
In this Chapter, we introduce some rich information resources which we are using
or we have built, and then compare these resources with other resources. The resources which we introduce are Japanese Ontology Goi-Taikei (Section 2.1), Valency(Semantic Pattern) Dictionary (Section 2.2), Japanese Semantic Database Lexeed (Section 2.3), and Japanese Treebank Hinoki (Section 2.4).
We show the overview of our resources in Figure 2.1. As shown in the figure, theseresources are related to each other and we are constructing deep parsers based on theseresources.

2.1 Japanese Ontology: Goi-Taikei
NTT has developed the Japanese-to-English Machine Translation System: ALT-J/E.
For ALT-J/E, several resources have been developed: that is, Japanese Semantic Word
Dictionary, Japanese-to-English Dictionary, Valency (Semantic Pattern) Dictionary,and Japanese Ontology (Goi-Taikei, Ikehara et al. (1997)). In this section, we introduce Goi-Taikei, which is related to the other resources we introduce in the following
sections.
According to Gruber (2008), ontologies are typically specified in languages thatallow abstraction. To treat the huge variety of language usage, abstraction is important.
Abstraction is effective to alleviate the data sparseness problem. In the case of ALT-J/E,
to allow abstraction, several information and restrictions are written by using definedclasses in the ontology.
7

Figure 2.1: Overview of our Resources and Aims

Goi-Taikei is originally developed for the purpose of Japanese text-to-speech, but

has been used extensively for Japanese-to-English machine translation. Because it hasvery wide coverage, it is applicable to versatile applications and systems. For otherontologies, see Section 2.1.1.
The Goi-Taikei Ontology consists of a hierarchy consisting of 2,710 semantic classes,
defined for over 264,312 nouns, with a maximum depth of 12 (from Level 0 to Level
11). We show the top 4 levels of the Goi-Taikei Common Noun Ontology in Figure 2.2.
The more specific classes are at deeper levels.
Table 2.1 shows the number of classes at each level of Goi-Taikei. It shows that
Level 7 gives the largest number of semantic classes.
Because many words have multiple senses, in Goi-Taikei’s Word Dictionary, eachushi “beef/cow” isword can be linked to up to 5 classes. For example, the word
linked to 2 classes; h537:beasti and h843:meat and eggsi. The first category has
the higher priority. On average, 1.2 classes are linked per word. We show some more¤

8

Lvl 0

Lvl 1

Lvl 2
3:agent

2:concrete

388:place533:object
1001:abstract
thing

1:noun1235:event

1000:abstract

2422:relation

Lvl 3
4:person362:organization
389:facility
458:region
468:natural place
534:animate
706:inanimate
1002:mental state
1154:action
1236:humanactivity
2054:phenomenon
2304:natural
phenomenon
2423:existence
2432:system2443:relationship2483:property
2507:state2564:shape2585:amount2610:location
2670:time

Figure 2.2: Top four levels (Lvl 0-3) of the Goi-Taikei Common Noun Ontology

9Table 2.1: Number of Classes at each Levels of Goi-Taikei
Lvl
0
1
2
3
4
5
6
7
8
9
10
11

No. of Sample of Semantic Classes
Classes
1
2
10
21
106
256
536
828
687
211
40
16

h1:common nouni
h2:concretei,h1000:abstracti
h3:agenti,h1001:abstract thingi
h4:personi,h1002:mental thingi
h5:humani,h1003:intellectual producti
h6:grammatical personi,h1004:study, departmenti
h7:1st personi,h1005:general field of studyi
h8:1st person singlei,h1010:commentaryi
h9:1st person single malei,h1011:discussion/disputei
h115:colleague/friendi,h1274:regretih116:associate/comrade/pali,h1950:paintingih1960:cultivationi

data about Goi-Taikei, in Appendix A.

2.1.1 Comparison with Other Resources
There are several thesauruses and ontologies besides Goi-Taikei. For English, there is
the most popular ontology, WordNet (Fellbaum, 1998). In WordNet, words (nouns,
verbs, adjectives and adverbs) are grouped into sets of cognitive synonyms called
synsets, each of which represents a distinct concept. The synsets are interlinked bymeans of the concepts. WordNet is separated by POS. Based on WordNet 1.5, EuroWordNet1 which is a multilingual database, is constructed. EuroWordNet includes
several European languages (Dutch, Italian, Spanish, German, French, Czech andEstonian, etc.). EuroWordNet and WordNet are linked each other. Construction ofJapanese WordNet project is now on going in NiCT (National Institute of Information
and Communications Technology) (Bond et al., 2008).
1 http://www.illc.uva.nl/EuroWordNet/

10

For Japanese, besides Goi-Taikei, there are famous ontologies, EDR (EDR, 1990)
and Bunrui-Goihyo (Kokken, 2004). The EDR (EDR, 1990) is composed of five typesof dictionaries (Word, Bilingual, Concept, Co-occurrence, and Technical Terminology), as well as the EDR Corpus. In the case of EDR, we can regard the Concept dictionary as an ontology. This ontology allows multiple inheritance, and is not necessary
in the form of a tree. As with Goi-Taikei, EDR has Word dictionaries and JapaneseEnglish Bilingual dictionary. Goi-Taikei is developed for the purpose of Japanese-toEnglish machine translation. Therefore, Goi-Taikei’s semantic classes are defined bycomparison with English translation. But in contrast, EDR is a general purpose dictionary not depending on specific application and algorithms. Therefore, it’s concepts are
distinguished word by word.
Bunrui-Goihyo is a collection of words classified and arranged by their meanings.
Bunrui-Goihyo is a thesaurus of 5 level tree structure. All of the words are classified
into classes at the leaves of the tree.
To compare thesauruses/ontologies, we show the size and features of them in Tables 2.2 and 2.3. We used following version for Comparison: Goi-Taikei is Common
Noun Ontology2, EDR is ver. 1.5., Bunrui-Goihyo is revised and enlarged edition, and
WordNet is ver. 2.0.
As shown in Table 2.2, the number of classes is larger than that of words, because
the EDR Dictionary has some classes (concepts) which have no words but have definitions.
Comparison based on examples
To see the differences between those ontologies (thesauruses), we show the class(es)for the wordushi “beef/cow” as an example. In Goi-Taikei, the wordushi
“beef/cow” is linked to two classes; h537:beasti and h843:meat and eggsi (h537:
beasti has higher priority). The hierarchic structure of h537:beasti is as follows:
h537:beasti ⊆ h536:animals (organism)i ⊆ h535:animali ⊆ h534:animatei⊆ h533:objectsi ⊆ h2:concretei ⊆ h1:common nouni. Therefore, h537:beasti
is at Level 6. We also show the entries of Goi-Taikei’s Japanese-to-English transfer
Dictionary, there are 4 translations bull, cow, cattle, ox for ushi. In the transfer dictionary, ushi is only translated as cow; niku,
gyûniku andbı̂fu have

¤

¤

¤

î

¤î

¤

2 In Goi-Taikei, there are other thesauruses for verbals and proper nouns.

11

ª¬

Table 2.2: Comparison of Goi-Taikei and Other Thesauruses/Ontologies: Size
Resource

DepthClasses
Words Words/ClassNo.
No.
Max Ave.
Goi-Taikei
122,715 300,000 93,141 110.5
Bunrui-Goihyo 5895 96,000 1,064 112.9
around 10 410,000 194,000 10,023 37.1
EDR
WordNet93,000 166,00028
1.8Because WordNet is not a tree, we can’t count the depth.

Table 2.3: Comparison of Goi-Taikei and Other Thesauruses/Ontologies: Class andTargetResource

Top Node(s)Class
Target POS(including largest
No. of words)
h464:jurisnoun
dictioni(proper noun,
verb,adj,adv)
hpersoniALLh1:common nouni(h1:proper nouni,
h1:eventi)
Bunrui-Goihyo hnominali, hverbali,
haspecti, hotheri
EDR
hconcepti
POS
WordNetGoi-Taikei

hMISCihbody partsiALLnoun,verb,adj,adv

the translation beef .
In the case of Bunrui-Goihyo, there are 2 entries which have different readings:
that isushi “cow” andgyû “beef”.
ushi “cow” is classified as hmammalsi(hmammalsi ⊆ hanimalsi ⊆ hnatural objects and phenomenai ⊆ hnominali).
and gyû “beef” is classified as hfish·meati (hfish·meati ⊆ hfoodi ⊆ hproducts
and toolsi ⊆ hmammals of nominali).

¤

¤

¤

¤

¤

ushi “cow”3 has one concept and the shortest path fromIn the case of EDR,
the top node (hconcepti) is as follows: hcattlei ⊆ hmammalsi ⊆ hvertebratei ⊆3 in EDR, cattle is used for¤ ushi12

hanimals (one of a species)i ⊆ hanimalsi ⊆ hagenti ⊆ hconcepti. Therefore, ushi “cow” (= hcattlei) is at Level 6 in the shortest path. Note that there isgyûniku “beef” (= hbeefi).
another concept beef ,Because EDR allows multiple inheritance, the parents of hanimals (one of a
species)i are not only hanimalsi but also hspecies of animate lifei, and theparents of hanimalsi are not only hagenti but also hanimate lifei. Note that all theabove concepts except hcattlei play the role of definition only (i.e., it corresponds to
no word).
In the case of WordNet, cow has 3 senses, but the sense 3 has figurative meaning. The sense 1 of cow is synset hcow, moo-cowi, the depth is 17, and it’s hierarchic structure is as follows: hcow, moo-cowi ⊆ hcattle, cows, kine, oxen,
Bos taurusi ⊆ hbovinei ⊆ hbovidi ⊆ hruminanti ⊆ heven-toed ungulate,artiodactyl,...i ⊆ hungulate, hoofed mammali ⊆ hplacental, placental
mammal,...i⊆ hmammal, mammaliani ⊆ hvertebrate, craniatei ⊆ hchordatei⊆ hanimal, animate being,...i ⊆ horganism, beingi ⊆ hliving thing, animate thingi ⊆ hobject, physical objecti ⊆ hphysical entityi ⊆ hentityi.
Sense 2 of cow is synset hcowi, the depth is 10, and it is located immediately below
hplacental, placental mammal,...i.
From above examples, we can summarize as follows: hyper semantic classes likehanimalsi and concrete leaf concepts like hcowi are common and among different
thesauruses. But they differ from each other in the hierarchical path. Bunrui-Goihyo’s
depth is 5, the classification is the coarsest, besides that, it includes not only content
words but also function words. EDR’s concepts have the finest granularity, and allows
multiple inheritance. WordNet’s hierarchy is the deepest. In the case of cow, both biological and general classification are mixed. Goi-Taikei Ontology has some advantages
that it has large coverage, easy-to-use tree structure, and a lot of related resources.
However, as shown above, because the thesauruses and ontologies have different
features, they should be used for various roles in different systems. In this thesis, weuse both Bunrui-Goihyo and Goi-Taikei for word sense disambiguation (Chapter 6),
because Bunrui-Goihyo includes function words.Then, WordNet (and EuroWordNet)
will play an important role in translation and multilingual systems.

¤

¤î

13

2.2 ALT-J/E’s Valency (Semantic Pattern) Dictionary
Detailed information about verb valency (subcategorization) and selectional restrictions is useful both for monolingual parsing and selection of appropriate translations inmachine translation. In addition to its usage in resolving parsing ambiguities (Ikehara
et al., 1991; Korhonen, 2002), verb valency information is particularly important forcomplicated processing such as detection and referent identification of zero pronouns(Nakaiwa and Ikehara, 1995; Yamura-Takei et al., 2002). Therefore, several dictionaries which have valency information have been constructed. In this section, we introduce the valency (pattern) dictionary from the Japanese-to-English machine translation
system ALT-J/E. For details of other valency dictionaries, see Section 2.2.1.
The ALT-J/E’s valency semantic (pattern) dictionary’s basic structure of a clausecomes from the relationship between the main verb and nouns. The structure transfer
dictionary provides this basic clause structure.
ALT-J/E provides 13,000 patterns for the valency dictionary and 3,000 patterns forthe idiomatic structure dictionary. In the valency dictionary, there are, on average, 2.3
patterns for each Japanese verb. When we ignore all idiomatic and adjectival patterns
there are 5,062 verbs and 11,214 valency patterns (2.2 patterns/verb).
We show some examples in Figures 2.3 and 2.4, whose head verbs are the same (shirei-suru “order”), but the structures are different.
As shown in Figures 2.3 and 2.4, each pattern consists of source (Japanese) andtarget (English) language subcategorization information and selectional restrictions on
the source side. Each argument on the Japanese side consists of head-word, a caserole, a list of case-markers and a list of selectional restrictions. There is also otherinformation about aspectual class, verbal semantic attributes and so on, which we will
not discuss here, although it is included in the patterns we create. Selectional restrictions are given as either nodes in the Goi-Taikei thesaurus (2,710 semantic classes;
see Section 2.1) or strings. It takes an expert lexicographer an average of 30 minutes
to create one pattern from scratch.
Because the valency dictionary is a transfer dictionary, the arguments associatedwith the predicate are linked between the two languages with indices (N1, N2, . . . ).
Each case-slot has information such as grammatical function, case-marker, case-role(the index number gives the case-role), semantic restrictions on the filler and default
order (not all the features are shown in the examples). Most arguments are NPs or PPs,

Æ2d

14





PATTERN -ID (PID)

203348S EMANTIC C LASS




















JAPANESE




























E NGLISH















N2/S10:ACC N3/N5/N8:ACC)




PRED
shirei-suru


POS

verb








CASE - ROLE
Agent






CASE - MARKER

N1

ga
“
NOM
”








RESTRICTION
hagentsi







CASE - ROLE
Object








CASE - MARKER
N2
o
“
ACC
”





RESTRICTION
hhuman activitiesi 









CASE - ROLE
Patient






CASE - MARKER

N3

ni
“
DAT
”






RESTRICTION
hagentsiPRED order




POS


verb



 






FUNCTION subject





N1





CASE
nominative 












FUNCTION direct-object 
N2








CASE
accusative 











FUNCTION that clause





N3


(mental transfer: N1:NOM

Æ2d

%
k
D

CASE

Figure 2.3: Valency (Semantic Pattern) Entry for the verb
order No.1 (SVOP)

15

Æ2d shirei-suru ⇔

PATTERN -ID (PID)

203346S EMANTIC C LASS




















JAPANESE




























E NGLISH















N2/S10:ACC N3/N5/N8:ACC)




PRED
shirei-suru


POS

verb




 


CASE - ROLE
Agent



 

CASE - MARKER
N1

ga “NOM”


 




RESTRICTION
hagentsi


 




CASE - ROLE
Patient



 



 
CASE - MARKER
N3
ni
“
ACC
”
 




RESTRICTION
hagentsi










CASE - ROLE
Quotation








S10 
CASE
MARKER
to
“
QUOT
”





RESTRICTION
h*i





PRED order




POS


verb



 






FUNCTION subject





N1





CASE
nominative 












FUNCTION direct-object 
N3








CASE
accusative 











FUNCTION clause





S10


CASE
quotative(mental transfer: N1:NOM

Æ2d

%
D
A

Figure 2.4: Valency (Semantic Pattern) Entry for the verb
order No.2 (SVPC)

16

Æ2d shirei-suru ⇔but it is possible to have a sentential argument, as in Figure 2.4, where it is marked
with S10. The arguments correspond between Japanese and English, thus giving the
backbone of the transfer. It is possible for an argument to only appear on one side, thisis useful for verbs in one language that incorporate information given as an argumentin the other.
We call the combination of case-role and case-marker the slot-type. A verb’s basic argument type is given by the combination of slot-types it allows. For example:
N1:agent+ga is one slot-type, N2:object-1+o is another, and their combination isthe basic transitive frame-type: N1:agent+ga, N2:object-1+o.
Because the Japanese slot-type combinations have not been treated as fixed caseframes, there are many minor variations, such as N1+ga with N3+ni and N1+ga with
N3+ni/e which are treated as different. In most cases, these are unmotivated distinctions, and it would be advantageous to merge them, as suggested by Nomura and Muraki (1996) and Baldwin et al. (1999). This would serve to reduce the number ofdifferent frame-types.

2.2.1 Comparison with Other Resources
There are several dictionaries which have valency information, especially monolingual
dictionaries. For English, there are VerbNet (Kipper et al., 2006) and FrameNet (Johnson et al., 2002) constructed based on extended verb classes (Levin, 1993). VerbNetgroups verbs based on their semantic or syntactic features. Each verb class in VerbNet
is described by thematic roles (29 roles:Agent, Patient, Location, etc.), selectional
restrictions on the arguments, and frames consisting of a syntactic description and semantic predicates with a temporal function.
For example, in VerbNet, the following roles and restrictions are described for order: Agent[+animate or +organization] V Patient[+animate or +organization] Proposition,
Agent[+animate or +organization] V Proposition[+oc to inf], andAgent[+animate or +organization] V Proposition[+that comp,-tensed that].
And the members of first type are followings: allow, call, need, okay, want, permit,
summon. A few hundred of verbs are recorded in VerbNet.
VerbNet and FrameNet are part of the SemLink project. SemLink is a project whose
aim is to link together different lexical resources via a set of mappings. Currently,
17through the SemLink project, mapped resources are followings: PropBank (Palmer
et al., 2005), VerbNet (Kipper et al., 2006), FrameNet and WordNet (Fellbaum, 1998).
Now, PropBank is a corpus which is annotated with around 4,500 argument rolelabels for around 3,300 verbs. For example of order4 : [ARG0 It] [ARGM−DIS also][rel ordered] [ARG1 P&G] [ARG2−PRD to produce more studies ...]
[ARG1 the plants] [rel ordered] * by [ARG0−by Florida Power]
For Japanese, EDR Japanese Co-occurrence Dictionary is a collection of verbs andrelated frames, which are extracted from corpus, and the number of records are 14,000
for 5,000 verbs.
IPAL verb/adjective dictionaries (IPA, 1987, 1994) classifies 861 Japanese basic

verbs and 136 basic adjectives based on semantic and syntactic features. Each verb
and adjective has case frame information, example sentences and so on. The IPAL
basic verb list has fine information, but does not have enough coverage of valency
patterns.
Both EDR and IPAL are hand-made dictionaries. There are some automaticallycreated frame dictionaries. For Japanese, Haruno and Yamazaki (1996); Utsuro et al.
(1997) extracted case frame information from syntactically annotated corpora. Kawahara and Kurohashi (2005) constructed a very large case frame dictionary from raw
corpus. They parsed the raw corpus automatically, then constructed the dictionary using a highly reliable part of the parsed results. They used newspaper text (Kawahara
and Kurohashi, 2005) or Web corpus (Kawahara and Kurohashi, 2006).
As above, though there are several hand-made or automatically created dictionaries, ALT-J/E’s valency dictionary is a one of the largest hand-made valency dictionaries. It has both fine grained and bilingual information.

2.3 Japanese Semantic Database: Lexeed
Because to build a richly informative dictionary by hand is both time consuming andcostly, so we select basic words to assign hand-made rich information. We call theJapanese Semantic Database of the basic words, Lexeed.
In this section, we introduce Lexeed, see Section 2.3.1 for other dictionaries.
4 There are 130 examples of order in PropBank.

18

The Lexeed Semantic Database of Japanese (Kasahara et al., 2004), which consistsof all words with a familiarity greater than or equal to five on a scale of one to sevensame as Goi-Tokusei (Amano and Kondo, 1998). This gives 28,000 words, divided
into 46,347 different senses (the fundamental vocabulary). The examples of Lexeed
are given in Figure 2.6 and 2.7. Each sense has a definition sentence and example
sentence written using only these 28,000 familiar words (and some function words).
In the case of the definition sentence of
1 (Sense 1 of doraibâ “driver”), an′
originarl sentence S1 is rewritten to S1 . Many senses have more than one sentence inthe definition: there are 81,000 defining sentences in all.

À{§

Each entry contains the word itself and its part of speech (POS) and the familiarity
score along with definition and example sentences. In addition, it’s also added some
information by Hinoki project: its lexical type(s) in the grammar, links to other senses
in the lexicon (such as hypernym), links to other resources (such as the Goi-Taikei,
WordNet (Fellbaum, 1998), Iwanami (Nishio et al., 1994)). (In Figure 2.6, 2.7, allunderlined features are added by the Hinoki project. See Section 2.4 for more details
about Hinoki). Then we show the overview of links between the linguistic resources in
Figure 2.5.
Each sense is linked with semantic classes of Goi-Taikei (Ikehara et al., 1997);
e.g.
1 (Sense 1 of doraibâ “driver”) is linked with h942:tooli, both2 and
1 are linked with h292:driveri,
3 is linked
withh921:plaything, sporting goodsi. Through the semantic classes, we cangather similer word senses; e.g. the senses which is linked with h292:driveri arefollowings,
1,2 untenshi “motorman”,
1 untenshu “chauffeur”,
1 kicho “chief pilot”,
1,2 sendo “boatman”,
1 tesuto pairotto “testpilot”,
2,3 pairotto “pilot”,
1 hikoushi “airman”,
2 doraibâ
“driver”,
1 pêpâ doraibâ “Sunday driver”,
1 raidâ “rider”,
and1 uchuuhikoushi “astronaut”. The semantic classes are principally defined for nouns (including verbal nouns), although there is some information for verbs
and adjectives. All content words of Lexeed, including nouns, verbs and adjectives, arelinked to semantic classes, as shown in Figure 2.6

À{§
À{§
þU3

þU¹
Ä¡
¨{Ä
±¨ À{§
æ ¥¹

À{§

¥¹

þU3
¨{Ä

Xò

À{§
À{

In addition, Lexeed senses are arranged into several hierarchies (Nichols et al.,2005, 2006). These are automatically produced from the word definitions, and do notlink all the senses into a single hierarchy. For example, in Figure 2.6 and 2.7, the19Figure 2.5: Overview of links between the Linguistic ResourcesÀ{§ °0À{ § À­{ § Â¬

À {§

þU3

hypernym for
1 is
1 equipment “tool”, for
2 and
1 is
1 hito “person”, for
3 is
2 kurabu “club”, and the domain for
3 is
1 gorufu “golf”.
The Lexeed entries can be searched via a web interface which I made5 . Figure 2.8
shows the interface. As shown in Figure 2.8, the interface shows the several links foreach word; that is hypernyms, tagged information, semantic classes (in Goi-Taikei),
English translations, and so on.
Lexeed is used for two things. First, it defines the sense inventory used in thesensebank and ontology. Second, the definition and example sentences are used ascorpora for the treebank and sensebank (See Section 2.4). Thus, as shown in Figure 2.6,
2.7, each content word in the definition and example sentences is annotated with sense
tags from the same lexicon, as well as syntactic information, as part of the Hinoki.
5 This interface is internal use only.

20

À



À{§ doraibâ

H EADWORD



POS

FAMILIARITY













S ENSE 1























S ENSE 2























S ENSE 3










noun















S


 1






screw turn (screwdriver)





 ′



S 1
2
2










2
2






A tool for inserting and removing screws . 






1 equipment “tool”






h942:tooli37515,0-0-1-0 (L ≃ R(3/3))






1
1
1





A person who drives a car





ò
H
,®
C
À{§
A0?
Ì¹
.
e
8



1
1
2
1
19




My farther was commended as a good driver




1 hito “person”
h292:driveri

37515,0-0-2-0 (L ≃ R(3/3))







1
1
3






 In golf, a long-distance club.










2
1







A number one wood .






kurabu“club”

2






1 gorufu “golf”





h921:plaything, sporting goodsi
37515,0-0-3-0 (L ≃ R(3/3))

Lexical-type

6.5 [1–7]







D EFINITION








H YPERNYMS EM . C LASSI WANAMID EFINITION






E XAMPLEH YPERNYMS EM . C LASSI WANAMID EFINITION







H YPERNYMD OMAINS EM . C LASSI WANAMI

noun-lex

F1 Wh0 

F1 k F0óe 8 
e&1< 8 2d ° °¥ k þU 2d 0 0Â¬   2 ® X G À­ Í } À­Â¬

Where L is an abbreviation of Lexeed, and R is an abbreviation of Iwanami (RWCP).

Figure 2.6: Entry for the Word
glosses)À{§ doraibâ “driver” from Lexeed (with English
21

H EADWORD


POS


FAMILIARITY














S ENSE 1










þU3 untenshu
noun















1
1
1
4






a person who drives trains and cars






5
1
1
6






3




I dream of growing up and becoming a train driver 




4 hito “person”
h292:driveri
-

Lexical-type

6.2 [1–7]
D EFINITION







E XAMPLE






H YPERNYMS EM . C LASSI WANAMI

Figure 2.7: Entry for the Word
glosses)noun-lex\ ℄ ¥ k þU 2d 0 

d&( C< 8b \ G þU3 D od G %Ý 2 

0

þU3 untenshu “chauffeur” from Lexeed (with English

22

Figure 2.8: Search Interface for Lexeed:

23À{§ doraibā “driver”

2.3.1 Comparison with Other Resources
Besides Lexeed, there are a lot of monolingual machine readable dictionaries: For
Japanese, Goi-Taikei’s Japanese Dictionary, JUMAN’s Dictionary (Kyoto University,2008), Iwanami (Nishio et al., 1994), and so on. JUMAN6 is a Japanese morphologicalanalysis system. Selected words JUMAN’s dictionary has around 30,000 selected basic
words (except proper nouns) which are assigned several information by hand, but has
no definitions. Iwanami defines the sense inventory used in the RWCP sensebank(originally used in the SENSEVAL-2 competition).
To compare these dictionaries, we show their size in Table 2.4. Table 2.4 shows thatthe size and features of Lexeed is the smallest but the related sensebank is the biggest.
Table 2.4: Comparison Lexeed and Other Resources
Resource
LexeedIwanamiJUMAN

Definitions Selected
Y
Y
N

Y
N
Y

Size of Resource Size of Sensebank
Entries Senses(No. of Words)
28,000 46,000
840,000
60,000 85,000
149,000
30,000-To compare Lexeed and Iwanami, and also to export into RWCP into Hinoki Sensebank, we linked Lexeed word senses (except function words) to Iwanami word senses,
and classified the link types by hand: that is completely same meaning (=), almost
same meaning (≃), Lexeed sense has wider meaning (⊃), Lexeed sense has narrower
meaning (⊂), meanings of Lexeed and Iwanami overlap (overlap), no sense to match(¬), others (others)7 . For example, each
1,2,3 doraibâ “screwdriver/driver/club” (Figure 2.6) has the almost same sense (≃) in Iwanami. In Figure 2.6, we show
this link as L ≃ R (3/3): where L is an abbreviation of Lexeed, and R is an abbreviationof Iwanami (RWCP), and (3/3) means that the judge is done by 3 of 3 evaluators. But
untenshu “chauffeur” (Figure 2.7) doesn’t appear in Iwanami.
We show the details of this classification results in Table 2.5. Though many Lexeed
word senses are linked to many Iwanami senses, in Table 2.5, the link types are clas-

À{§

þU3

6 http://www-lab25.kuee.kyoto-u.ac.jp/nl-resource/juman.html
7 others includes idiom, no entry of same index word, etc.

24

sified in order of following priority: =, ≃, ⊃, ⊂, overlaps, 6=, others. Table 2.5 showsthat 69.4% of Lexeed have same or almost same Iwanami sense, and Lexeed meanings
tend to be narrower than Iwanami.
Table 2.5: Hand-Classification of Link types of Lexeed and Iwanami
Type
Lexeed = Iwanami
Lexeed ≃ Iwanami
Lexeed ⊃ Iwanami
Lexeed ⊂ Iwanami
Lexeed overlaps Iwanami
Lexeed 6= Iwanami

othersTotal

No. of Lexeed word senses
No.
(%)
318
0.8
27,620
68.6
2,044
5.1
6,554
16.3
473
1.2
2,895
7.2
376
0.9
40,280100

To compare Lexeed and JUMAN’s dictionary, we automatically linked them using
lemmas and POS as pivots. Table 2.6 shows that the size of them and the entries whichis existing in both dictionaries. And Table 2.7 shows that the entries which is existingin only Lexeed or JUMAN.

25

Table 2.6: Number of Words Existing in both Lexeed and JUMAN
POS

JUMAN

Same Entries

LexeedNo.

Noun22,41924,634

Verb

4,225

3,160

Adj

2,350

498

Adv

1,246

668

Others

296

583

Total

30,53629,543Sample+¶ haigo “back”, Éw reitou “refrigeration”
2,474 «Zd shimeru “take”, ÊY hagemu “endeavor”
Wdaratamaru “be renewed”
344  fukai “profound”,
0 kuyashii “mortifying”
312 ß+? magete “distorted”,%å mojidori “literally”
124 9$b dakara “so”, flC ironna “several”

13,22816,482

Table 2.7: Number of Words Existing in Only One Dictionary (Lexeed or JUMAN)
POS

No.

Samples

JUMAN Only

Others÷ ninkan “appointment”, fe tosei “living”, ÈÖ chushi “gaze”
1,800 ü( tumazuku “stumble”, Ǒ2 shosu “deport”, d nejiru “twist”
2,014 ã$9 fuyukai-da “unpleasant”, Þ wakenai “easy”
935 f(f( rokuroku “uselessly”, ©b3 wareshirazu “unconsciously”
173 ÇdR& osorubeki “fearful”, ¬3R& hazubeki “shameful”

Total

14,540

Noun
Verb
Adj
Adv

9,618

Lexeed Only

Noun
Verb
Adj
Adv
OthersTotal´ osoban “late shift”, (0 kushi “comb”,  hakkâ “hacker”
686 Û<8(d hittakuru “take by force”, Af*d torokeru “melt”
154 N.h0 fusawashii “suitable”, >0 takumashii “robust”
356 <? chikatte “upon my honor”, W: imaichi “lack something”
459 a0a0 yoshiyoshi “huba-huba”, \Cb nazenara “because”

11,406

13,06126

2.4 Japanese Treebank / Sensebank: Hinoki
In this section we describe the Hinoki corpus. The corpus is built from the Lexeed
dictionary definitions, examples and newspaper text. It consists of the treebank andsensebank. The treebank uses an HPSG based Japanese grammar to encode both syntactic and semantic information. The sensebank uses Lexeed as a sense inventory.
The target corpus of treebank and sensebank is the same, so the Hinoki corpus has
an important advantage over general treebank and sensebank, in that it can provide
syntactic, semantic and lexical semantic information.

2.4.1 Syntactic Annotation
Syntactic annotation in Hinoki is grammar based corpus annotation done by selecting
the best parse (or parses) from the all analyses derived by a broad-coverage precision
grammar. The grammar is an HPSG implementation (JACY: Siegel and Bender, 2002),
which provides a high degree of details, marking not only dependency and constituent
structure but also detailed semantic relations. As the grammar is based on a monostratal theory of grammar (Head Driven Phrase Structure Grammar: HPSG, Pollard andSag, 1994), annotation by manual disambiguation determines syntactic and semantic
structure at the same time.
First, the corpus is parsed, and then the annotator selects the correct analysis (or,occasionally rejects all analyses). Selection is done through a choice of discriminants.
The actual annotation process uses the same tools as the Redwoods treebank of English(Oepen et al., 2004) which was parsed by HPSG-based English grammar (ERG) (SeeFigure 2.1 for the relation with our resources). The system selects features that distinguish between different parses, and the annotator selects or rejects the features until
only one parse is left. Using a grammar helps treebank consistency — all sentences
annotated are guaranteed to have well-formed parses. The flip side to this is that any
sentences which the parser cannot parse remain unannotated, at least unless we were
to fall back on full manual mark-up of their analyses. The average number of decisions
for each sentence is proportional to its length (around log2 of the number of parses). In
general, even a sentence with 5,000 parses requires around 12 decisions (Tanaka et al.,2005a). Table 2.8 shows the size of target corpus of Hinoki project.
The Hinoki treebank currently consists of around 95,000 annotated definition and27

Table 2.8: Size of Hinoki’s Target Corpus
CorpusSentences
Definitions
75,000
Examples
45,000
RWCP
36,000Kyoto
38,000Words Content words Basic words
691,072
318,181318,181
498,977
221,224
221,224
888,000
692,069
391,010
969,558
526,760
472,419

example sentences of the Lexeed dictionary. The definition and example sentences inthe dictionary are short, around with 10 words on average, and are relatively self contained. The example sentences are relatively easy to parse. The definition sentences
contain many coordinate structures and are relatively hard to parse. We are currently
parsing and annotating newspaper text (Kyoto Corpus and RWCP Corpus) and 25% areparsed, of with around 50% are correct. We extended JACY by manually adding the
Lexeed defining vocabulary, and some new rules and lexical-types, to parse dictionary
sentences (Bond et al., 2004a). We still need more grammar roles and lexicon development for newspaper text. See Bond et al. (2006); Tanaka et al. (2005a) for anotation
details.
Now, we show the information included in Hinoki treebank. As an example, we use
the definition sentence ofuntenshu “chauffeur: somebody who drives trains andcars” (Figure 2.7).

þU3

There were 4 parses for the definition sentence. The correct parse, shown as aphrase structure tree, is shown in Figure 2.9. The two sources of ambiguity are theconjunction and the relative clause. The parser also allows the conjunction to combine
densha “train” and hito “person”. In Japanese, relative clauses can have gappedand non-gapped readings. In the gapped reading (selected here), hito “person” is theunten “drive”. In the non-gapped reading there is some underspecified
subject ofrelation between the modifee and the verb phrase. This is similar to the difference inthe two readings of the day he knew in English: “the day that he knew about” (gapped)
vs “the day on which he knew (something)” (non-gapped). Such semantic ambiguity
is resolved by selecting the correct derivation tree that includes the rules applied inbuilding the tree (Figure 2.10).

\

0

0

þU

28

UTTERANCE
NP
VP

N

PP

V

NP
PP
N

CONJ

densha
train

\

℄

N

¥

CASE-P

k

V

V

þU 2d

ya
or

jidousha
car

o

unten
drive

suru
do

ACC

N

0
hito
personþU31 “chauffeur”: “a person who drives a train or car”
Figure 2.9: Syntactic View of the Definition of þU31 untenshu “chauffeur”
frag-np
rel-cl-sbj-gap
hd-complement

noun-lehd-complement

v-light

hd-complementcase-pacc-le

hd-complement
noun-le conj-le noun-le

\

℄

¥

k

densha
train

ya
or

jidoushacarovn-trans-le v-light-le noun-le

ACC

þU

2d

0

unten
drive

suru
do

hito
personþU31 “chauffeur”: “a person who drives a train or car”
Figure 2.10: Derivation Tree of the Definition ofþU31 untenshu “chauffeur”

Phrasal nodes are labeled with identifiers of grammar rules, and (pre-terminal) lexical nodes with classnames for types of lexical entries.

29

The semantic representation is Minimal Recursion Semantics (MRS) (Copestake
et al., 2005). We simplify this into a dependency representation, further abstracting
away from quantification, as shown in Figure 2.11. We can interpret the meaning of
Figure 2.11 as Figure 2.12. One of the advantages of the HPSG sign is that it contains
all this information, making it possible to extract the particular view needed. In orderto make linking to other resources, such as the sense annotation, easier predicates arelabeled with pointers back to their position in the original surface string. For example,
the predicate densha n 1 links to the surface characters between positions 0 and 3:.

\



2.4.2 Semantic Annotation
The lexical semantic annotation uses the sense inventory from Lexeed (See Section 2.3).
All words in the fundamental vocabulary are tagged with their sense. For example, theword yume “dream, hope” (of example sentence in Figure 2.7) is tagged as sense 3
in the example sentence, with the meaning “hope, wish”.

ÝBecause the Lexeed word senses are linked to Goi-Taikei semantic classes, we canget the semantic classes through the word senses. In the case of 3 yume “hope, wish”,
we can get not h1252:dreami but h1363:hope/wishi as a semantic class.

Ý

As above, the Hinoki Corpus (Bond et al., 2006) consists of dictionary definitionand example sentences (from Lexeed) and newspaper corpora (taken from the Kyoto
Corpus (Kurohashi and Nagao, 2003) and RWCP corpus.
Lexeed definition and example sentences consist of basic words and function words

only, i.e., it is self-contained. Therefore, all content words have headwords in Lexeed,
and all word senses appear in at least in one example sentence.
Both newspaper corpora were taken from the Mainichi Daily News. RWCP was the
text used for the Japanese dictionary task in SENSEVAL-2 (Shirai, 2002) (which has
the Senseval sense annotation). And the Kyoto Corpus is marked up with dependency
analysis (Kurohashi and Nagao, 2003). We chose these corpora so that we can compare our annotation with existing annotation. Both these corpora were already word
segmented and part-of-speech annotated.
Table 2.9 shows the size of the Hinoki sensebank. Words were segmented and mor30

e2:unknown<0:13>[ARG x5:_hito_n]x7:densha_n_1<0:3>[]
x12:_jidousha_n<4:7>[]
x13:_ya_p_conj<0:4>[LIDX x7:_densha_n_1,
RIDX x12:_jidousha_n]e23:_unten_s_2<8:10>[ARG1 x5:_hito_n]
e23:_unten_s_2<8:10>[ARG2 x13:_ya_p_conj]

Figure 2.11: Simplified Dependency View (MRS) of the Definition of
shu “chauffeur”

Figure 2.12: Interpretation for MRS ofþU31 unten-

þU31 untenshu “chauffeur” (Figure 2.11)31

phologically tagged using ChaSen8 . The Kyoto Corpus is originally morphologicallyanalyzed using Juman9. We converted the tags into the IPA tagset used in ChaSen.
Table 2.9: Size of Hinoki Sensebank

Sentences
CorpusDefinitions
75,000
Examples
45,00036,000
RWCP
Kyoto
38,000Content
words
318,181
221,224
692,069
526,760

Words
691,072
498,977
888,000
969,558

Basic Mono-semouswords%
318,18131.7
221,22430.5
391,01039.3
472,419
36.3

Now, we are adding annotation of Goi-Taikei’s class over Hinoki corpus, we’ve
finished the first half of the same part of Kyoto Corpus. We show the example in
Table 2.10.

2.4.3 Comparison with Other Resources
Treebank
There are some morphological and syntactical annotated corpora. For English, there
are EDR English Corpus (120,000 sentences), Penn Treebank (Marcus et al., 1994) (including Wall Street Journal, The Brown Corpus and so on), PropBank (Palmer et al.,2005) (85,000 sentences), which were added predicate-argument relations to the syntactic trees of the Penn Treebank. PropBank is also being mapped to VerbNet andFrameNet as part of SemLink: Mapping together PropBank/VerbNet/FrameNet. Hereis an example of PropBank:[ARG1 Commonwealth Edison Co.] was [rel ordered] *-1 [ARG2−PRD *-2 to refund about$ 250 million *U* to its current and former ratepayers for illegal rates collected * forcost overruns on a nuclear power plant].
For Japanese, there are Kyoto Text Corpus10 , NAIST Text Corpus (Iida et al.,8 http://chasen-legacy.sourceforge.jp/
9 http://nlp.kuee.kyoto-u.ac.jp/nl-resource/juman.html
10 http://nlp.kuee.kyoto-u.ac.jp/nl-resource/corpus.html

32

2007)11 , EDR Japanese Corpus (200,000 sentences), and balanced corpus are now
constructing by KOTONOHA project12 . Both Kyoto Corpus and NAIST Corpus areannotated to the same 40,000 sentences of newspaper; Mainichi 1995. About 5,000
sentences of Kyoto Corpus and all of NAIST Corpus have information about predicateargument and co-referential relations, but there target case is surface case only. Thenin the case of NAIST Text Corpus, the target case markers to annotate is following:ga, o, ni.
As above, Hinoki has not only predicate-argument information but also semantic information (MRS). But Hinoki has no information about co-referential relations.
Howevere we tag the same text, we can also use lexical semantic information.

%

k D

Sensebank
Several semantically tagged corpora are provided in the competitions for word sense
disambiguation; Senseval-1,2,3 and SemEval. In the case of Japanese, for SENSEVAL2’s Japanese dictionary task, RWCP corpus was provided. RWCP corpus are defined
word senses according to Iwanami Japanese dictionary (See Section 2.3.1). Of 888,000
words (Table 2.9), 148,558 words are tagged with Iwanami senses. So Hinoki sensebank has much bigger coverage over RWCP. However, annotated resources are stilllacking in several genres, therefore we need to investigate an efficient way to construct
resources.

11 http://cl.naist.jp/nldata/corpus/
12 http://www.kokken.go.jp/en/research projects/kotonoha/kotonoha/

33

Table 2.10: Simplified Example of semantic annotated Hinoki corpus
Word

Semantic Class9 p Murayama “family name”è¿ p Tomiichi “first name”< 1 shusyo “Prime Minister”
H ha “TOP”¡ nentou “beginning of a year”
D8 niatari “when”< 1 shusyo “Prime Minister”
81 kantei “official residence”
 de “LOC”
×1 naikaku “cabinet”
d1 kisha “reporter”
4 kai “meeting”
A to “with”
èd℄ 28 “28”
ñ4 nichi “day”
|1 kaiken “interview”
0 shi “did”
 , “punctuation”
u shakaitou “Socialist Party”
G no “of”
5 shin “new”
Ú0 p minshu “democracy”
å1 rengou “union”
-1 shozoku “belong”{Ø1 giin “cabinet member”
G no “of”...
The Sample Sentence is:

h47:men and women/
genderi
h48:male/manih260:politicianih2707:beginningih260:politicianih447:housing (Others)i
h364:executive agency/
administrative bodyi
h245:journalisti
h378:societyi
h2586:numberi
h2682:dayih1695:meetingi
h2050:executioni
h380:politicalpartyih2710:old and new OR
slow and fasti
h1014:beliefs/
principlei
h2229:unioni
h2475:dependencei
h260:politiciani
...

Type
B

Proper NounClassh260:politician

IE

h260:politicianih260:politicianiB

h380:politicalpartyiB

h382:faction/secti

Ih382:faction/sectiEh382:faction/secti

...

...

9è¿< H¡D8 < 8×dAèd℄ñ|0
uGÚ0å-{ØG. . .
34

Chapter 3
Extending the Coverage of a Valency
Dictionary
In this chapter, we present an efficient method of assigning valency information andselectional restrictions to entries in a bilingual dictionary, based on information in
an existing valency dictionary. The method is based on two assumptions: words withsimilar meaning have similar subcategorization frames and selectional restrictions; andwords with the same translations have similar meanings. Based on these assumptions,
new valency entries are constructed for words in a plain bilingual dictionary, using entries with similar source-language meaning and the same target-language translations.
We evaluate the effects of various measures of semantic similarity1.

3.1 Introduction
As mentioned in Section 2.2, detailed information about verb valency (subcategorization) and selectional restrictions is important for natural language processing tasks
such as monolingual parsing, accurate rule-based machine translation and automatic
summarization. However, this information is not encoded in normal human-readable
dictionaries, and is hard to enter manually. Therefore, for most of the language pairs,
the lack of suitable language resources is a severe problem. Even when word-lists or
1 First we reported in Fujita and Bond (2002a) about this method. Then we revised it in Fujita andBond (2002b) and Fujita and Bond (2004c). Then put them together into a journal, Fujita and Bond(2007).

35

simple bilingual dictionaries exist, it is rare for them to include detailed information
about the syntax and meaning of words.
Although great progress has been made in learning statistical models from annotated corpora, most commercial machine translation systems rely on detailed information compiled in lexicons. They are typically hand-built. However, adding suchdetailed information to dictionaries is both time consuming and costly.
In this chapter we present a method of adding new entries (we call these valency
patterns, or patterns) to a bilingual valency dictionary. New patterns are based on
existing patterns, so they have the same amount of detailed information. The method
bootstraps from an initial hand-built lexicon, and allows new patterns to be added
cheaply and effectively. Although we will use Japanese and English as examples, thealgorithm is not tied to any particular language pair or dictionary. The core idea isto add new patterns to the valency dictionary by using Japanese-English pairs from aplain bilingual dictionary (without detailed information about valency or selectional
restrictions), and to build new patterns for them based on existing patterns. We show
the basic method in an illustration, Figure 3.1. As shown in this figure, because there
are relatively large plain bilingual dictionaries, we extend hand-build bilingual valency
dictionary using such bilingual dictionaries.

Figure 3.1: Point of the Idea for Extending the Coverage of a Valency Dictionary36

3.1.1 The Number of Valency Patterns Required

Shirai (1999) estimates that at least 27,000 valency patterns are needed to cover around
80% of Japanese verbs in a typical newspaper. Various methods of creating detailed
patterns have been proposed, such as the extraction of candidates from corpora (Li andAbe, 1998; Haruno and Yamazaki, 1996; Manning, 1993; Utsuro et al., 1997; Kawahara and Kurohashi, 2001), the automatic and semi-automatic induction of semantic
restrictions (Akiba et al., 1995, 2000) and hand-construction (Dorr, 1997; Johnson
et al., 2002; Erk et al., 2003).
However, the quality of automatically constructed monolingual patterns is still far
from that of hand-constructed resources. Further, large-scale bilingual resources arestill rare for most language pairs, so that it is hard to automatically build bilingual
patterns.
Our work differs from corpus-based work such as Manning (1993) or Kawahara
and Kurohashi (2001; 2005) in that we are using existing lexical resources rather thana corpus, and we are obtaining selectional restrictions as well as subcategorization
frames. Our method is also applicable to rare words, as long as we can find them in abilingual dictionary, and know the English translation. It does not, however, learn new
frames from usage examples.
Our method adds new patterns by leveraging existing knowledge in the system
dictionaries. We illustrate the method with examples of building a Japanese-English
lexicon, but there is nothing in the method itself that is language specific. The basic
idea is to add new patterns to the pattern dictionary by using Japanese-English pairs
from a plain bilingual dictionary (without detailed information about valency or selectional restrictions), and build new patterns for them based on existing patterns.
We present both fully automatic and semi-automatic implementations in this thesis.
However, even the semi-automatic implementation does not rely on detailed knowledge of the system dictionaries by the analyst. Our method is similar in principle to
Ikehara et al. (1995) who add useful information to a user dictionary by comparing
input word pairs to existing patterns in the system dictionary.
37

3.1.2 Coverage of Original Valency Patterns
To test the useful range of our algorithm, we evaluated the coverage of ALT-J/E’svalency dictionary (See Section 2.2) on 9 years of Japanese newspaper text (6 years ofMainichi and 3 years of Nikkei)2 (see Table 3.1 and Figure 3.2; we graphed Table 3.1
as Figure 3.2). They consist of about 309,000,000 words. The coverage of tokens ishigh (92.5%), but many infrequent verb types are missing from our system (over 62%
of verb types have no pattern). The verbs which have no pattern appear on average 50
times. Many of these infrequent words are still quite familiar to native speakers. For
examplechoromakasu “steal”,
nareau “conspire” andhareagaru “swell up” appear only once in the 9 years of newspaper data, but they
are very familiar words. The existing dictionary’s coverage is good, but not complete.
We measure familiarity using Goi-Tokusei (Amano and Kondo, 1999) and web frequencies. Goi-Tokusei lists word familiarities for Japanese: The word-familiarity is asubjective rating score which represents how familiar people feel to a particular word.
The rating scale is a 7-point scale (1:unfamiliar – 7:familiar): 94% of adults know thewords those familiarities are higher than 5 (Amano and Kondo, 1998). The words cited
above all have word familiarities greater than 5. From web pages, we can find 27,100
choromakasu “steal”, 102,000 for
pages for the base form of
nareau “conspire” and 55,700 for
hareagaru “swell up”: these are widely3
used words.
In the newspaper text, there is an average of 3.1 verbs for each sentence. Therefore,
one verb in every 5 sentences has no pattern. In order to reduce the number of unknown
verbs to one in 10 sentences, we need to add valency information for 2,647 verbs.

%d:`fW$2àe:`fW$29eê%dTable 3.1: Cover Ratio for Japanese Newspapers (9 years)
In lexicon
No. of Types (%) No. of Tokens (%)
Japanese exists 4,997
37.6 24,656,590 92.5
No pattern
8,304
62.4 2,000,710
7.5
Total
13,301100.0 26,657,300 100.0

2 Mainichi ’91, ’92, ’94, ’95, ’99, 2000 and Nikkei ’95, ’96, ’98.
3 http://www.google.co.jp/, searched on 2006-03-22.

38

9eê

àe

Figure 3.2: Graph of Cover Ratio for Japanese Newspapers (9 years)

3.1.3 Utility of Valency Information
In order to demonstrate the utility of the valency information, we give an example ofa sentence translated with the system default information (basically a choice between
transitive and intransitive), and the full valency information in (1).4 The verb istanomu “ask” [NP-ga NP-ni Cl-to V], which takes a clause complement. Without
the valency information the translation is incomprehensible: the clause complement
is misinterpreted, the zero-pronoun is not resolved and the English to-infinitive is notproduced.

q

Y

(1)

=ý H .~

D . C aD ql9

Tarō wa tomodachi ni hanasa nai, yōni tanonda
Tarou TOP friend
DAT talk
not QUOT asked“Tarou asked his friend not to talk.”
with: Taro asked his friend not to talk.
without: As Taro did not talk to his friend, * asked.
4

We use the following abbreviations: NOM: nominative postposition; ACC: accusative postposition;

DAT : dative postposition; LOC: locative postposition; TOP : topic postposition; QUOT : quotative postpo-

sition; REC: reciprocal postposition; NP: noun phrase; Cl: clause; V: verb. The sentence is translatedusing ALT-J/E Ikehara et al. (1991).

39

In general, translation tends to simplify text, because the target language will not
be able to represent exactly the same shades of meaning as the source text: there issome semantic loss. Therefore, in many cases, a single target language entry is thetranslation of multiple similar source patterns.
For example, there are 23 Japanese predicates linked to the English entry reportin the valency dictionary used by the Japanese-to-English machine translation system
ALT-J/E Ikehara et al. (1991). Six of these have the same frame-type as that shown in
Figure 3.3. Five patterns have the frame-type shown in Figure 3.4. Three more link toa variation of that in Figure 3.3 where N3+ni is replaced by N3+ni/e/made. Collapsingsuch minor variations, 11 are of one type, 7 of the other, and only 2 are genuinely
different. Therefore, in order to make new frames for predicates that translate into
English report, we need to add only two patterns, one of the types in Figure 3.3 andone in Figure 3.4. Ideally, we should merge these into a single alternation (Levin,1993) and link to that as suggested in Baldwin et al. (1999).
The ultimate aim of this research is to identify what kind of information is mosteffective in the creation of lexical patterns. In particular we wish to discover what is
the minimal amount of information necessary to reliably create new patterns. Dillinger(2001) criticized previous research presented on lexical construction as paying “moreattention to theoretical issues than to establishing effective processes for dictionary
development”. We address both theory and practice through rigorous evaluation ofvarious methods with an emphasis on producing usable patterns as the final result.
In the following sections, first we propose the basic method of creating and refining new patterns (Section 3.2). Then we add two refinements: creating multiple
patterns simultaneously using information about alternations, and merging similar patterns (Section 3.2.4). We are able to create high-quality patterns cheaply. We test thevarious filters to improve the quality of patterns and to make the creation of patterns
more efficient. The evaluation (Section 3.3) is done with both a translation task-based
evaluation and a direct evaluation by lexicographers. We then discuss the results andsuggest a refined method, compare our research with other approaches and discuss
further work (Section 3.4). Finally, we conclude (Section 3.5).
40





PATTERN -ID (PID)

202969S EMANTIC C LASS




















JAPANESE




























E NGLISH















N2/S10:ACC N3/N5/N8:ACC)
joushin-suru
PRED



POS
verb







CASE - ROLE
Agent





CASE - MARKERN1ga “NOM”







RESTRICTION
hagentsi



 



CASE - ROLE
Object







CASE - MARKER
 
N2
o
“
ACC
”

 



RESTRICTION
habstracti 



 





CASE - ROLE
Patient



 



CASE - MARKERN3ni “DAT”






RESTRICTION
hagentsiPRED report





POS

verb



 






FUNCTION subject






N1




CASE
nominative 













FUNCTION direct-object 
N2







CASE
accusative 


 







FUNCTION that clause






N3

(mental transfer: N1:NOM

ê 2d

%
k
D

CASE

Figure 3.3: Valency (Semantic Pattern) Entry for the verb
report No.1 (SVOP)41

ê 2d joushin-suru ⇔

PATTERN -ID (PID)

202970S EMANTIC C LASS




















JAPANESE




























E NGLISH















N2/S10:ACC N3/N5/N8:ACC)
joushin-suru
PRED



POS
verb



 



CASE - ROLE
Agent



 

CASE - MARKERN1ga “NOM”

 





RESTRICTION
hagentsi



 



CASE - ROLE
Patient




 


CASE - MARKER
 
N3
ni
“
ACC
”

 



RESTRICTION
hagentsi










CASE - ROLE
Quotation








S10 
to “QUOT”
CASE - MARKER






RESTRICTION
h*i





PRED report





POS

verb



 






FUNCTION subject






N1




CASE
nominative 













FUNCTION direct-object 
N3







CASE
accusative 


 







FUNCTION clause






S10

CASE
quotative(mental transfer: N1:NOM

ê 2d

%
D
A

Figure 3.4: Valency (Semantic Pattern) Entry for the verb
report No.2 (SVPC)

42

ê 2d joushin-suru ⇔3.2 Method of Creating Patterns
3.2.1 Overview of Method of Creating Patterns
The method is based on the observation that verbs with similar meanings typically have
similar valency structures. That is, if there is an unknown verb (SU ) whose meaning
is similar to an existing verb in the seed dictionary (the known verb SK ), we can copy
the valency information of SK for SU . This method has some fundamental limitations.
It only creates valency patterns for words for which we can find similar words in thevalency dictionary. But it is simple and robust because it creates new patterns bycopying from the existing patterns.
The basic method used to determine semantic similarity is translation equivalence:
if two verbs have the same English translation then they have similar meanings. Thismassively overgenerates: one sense of a verb may overlap, but not all will. Further, thematch criteria are quite loose: any pattern with the same English head.5 Therefore giveup and give back are counted as the same entry. This allows for minor inconsistencies
in the target language dictionaries. In particular the valency dictionary is likely toinclude commonly appearing adjuncts and complements that do not normally appear
in bilingual dictionaries. For example:
iku “go” is translated as to go in EDICT,
go in the ALT-J/E word transfer dictionary and NP1 go from NP2 to NP3 in ALT-J/E’svalency dictionary (among other translations). To match these patterns it is necessary
to have some flexibility in the English matching.
A single verb may have multiple possible subcategorization (subcat) and selectional restrictions (SR). We create more patterns by using data about verbal alternations
Levin (1993): if the existing verb participates in a known alternation then we create
new patterns based on both alternatives (See Section 3.2.4 for more information).
To reduce the overgeneration, we investigate various methods of further constraining the creation of new patterns, such as a simple human check (pre-filter), paraphrasing, a multilingual check, semantic association scores and the strength of thelink through English. Then we investigate different ways to merge similar patterns.
Evaluation was done using both a task-based evaluation and checking by an expert
lexicographer. These methods are described in more detail in the following sections.

(

5 We exclude idiomatic patterns (those with fixed case slot fillers) and patterns headed by light verbs

such as make, do and take.

43

3.2.2 Constructing Candidates
To find translation equivalences, we used a plain bilingual dictionary which contains
word pairs without valency information. This was made from ALT-J/E’s JapaneseEnglish word transfer dictionary and an enhanced version of EDICT (Breen, 2004)
where Japanese verbal-nouns were expanded into verbs (e.g.,joushin “report”
was expanded into
joushin-suru “report”).6
To create a candidate SU , an Unknown word for which we have no valency information, we find all words where E, the English translation (or translations) is linked
to one or more valency patterns SK in the valency dictionary. Figure 3.5 shows theoverall flow of creating new patterns. We discuss the details about filtering methods in
Section 3.2.3
For example,
matou “wear [clothes]”, matching through the translation gives
15 candidate Japanese verbs in the valency dictionary on which we can base the new
kiru “wear”,
yowaru “wear [out]”,
entry. These include
warai-o ukaberu “wear a smile” and so on (some possible links are shown in Figure 3.6). Some of this variety of candidates comes from the polysemy of the English
kiru “wear” corresponds to WordNet sense 1 “be dressed in”,
yowaruverb:“wear out” to sense 8 “exhaust or tire though overuse or great strain or stress” andwarai-o ukaberu “wear a smile” to sense 3 “wear an expression of one’s
attitude or personality”. In fact,
matou “wear” corresponds only to sense 1.

ê

ê 2d

R

Àd

.d

Õkñ$Rd

Àdkñ$Rd.dÕR

3.2.3 Filtering Candidates
In order to filter out inappropriate candidates, we investigate several methods of judging similarity.
Pre-filter
The simplest method is to use human judgment. This is implemented as a pre-filter(reject) where an analyst examines the two source language words (SU , SK ) linked
by an English translation, and rejects them if they do not have a similar meaning.
6 EDICT typically translates Japanese verbal nouns as nouns, without giving a separate verb entry:

»

e.g.,
kyōdō “cooperation”. We used ALT-J/E’s English morphological dictionary and the EDICT
part-of-speech codes to create 10,395 new verb entries such as:
kyōdō-suru “cooperate”.

»2d

44

Step 1: For each pattern (SU -TU ) in the plain S − T dictionary with no pattern in thevalency dictionary• For each valency pattern (SK ) with the same target translation (TUK )– Create a candidate pair SU -SK
Step 2: For each candidate pair SU -SK (linked by TUK )• apply filtering methods Section 3.2.3• If the candidate pair doesn’t filter out (SU -SK )– Replace SK by SU then create a new pattern (SU -TUK ) for pairs

Step 3: For each new pattern SU -TU (made from SK -TK )1. If SK -TK has an alternation SA -TA
also create candidate SU -TA Section 3.2.4
2. If there are similar new patterns
merge them Section 3.2.4
Figure 3.5: Flow of Creating New Patterns

Many words that are obviously dissimilar are linked due to the polysemy of the English
pivot. Rejecting them is a very fast process. It only becomes slow if the analyst does
not recognize one of the verbs and therefore has to look it up. The strength of thismethod is its accuracy: the weakness is that it requires human intervention, and is thus
expensive.
Consider the three pairs shown in (2). For a Japanese native speaker, (a) is clearly
good, while (b) and (c) are clearly bad.
(2) Potential candidates fora.

R matou “wear [clothes]”.

R ⇔Àd (⇔ kiru “wear [clothes]”)45

Japanese (SU )Pivot(TUK )

R
matou

Japanese (SK )

Àd
kiru

wear
(English).dyowaruÕkñ$Rdwarai-o ukaberu
Figure 3.6: Creating Candidates through a Common Pivot TranslationR ⇔.d (⇔ yowaru “wear [out]”)c. R ⇔Õkñ$Rd (⇔ warai-o ukaberu “wear a smile”)

b.

Paraphrasing
The aim of this filter is to eliminate candidate patterns with incorrect subcats, without
having to use an expert bilingual lexicographer.
The filtering is done by an analyst. The analyst judges whether sentences with thecandidate verb (SU ) replaced by the seed verb (SK ) (and vice-versa) are grammaticalor not. Ideally, words with the same subcat will produce a grammatical paraphrase,while those with different subcats will not.
kekkon-suru “marry” (SK ) andtotsugu “marry
For example, bothkekkon-suru “marry” is a reciprocal
into”(SU ) have similar meanings. But
verb: “a man and a woman marry”,
totsugu “marry into” on the other hand isdirectional, “a woman marries a man/into a family” and thus the subcat is different.
kekkon-suru “marry” is replaced withThis can be seen in (3) and (4), where
totsugu “marry into”, but (4) is ungrammatical.

^42d

d)

(3)

^42d
d)

d)

^42d

 H  A ^42d

kanojo wa kare to kekkon-suru
sheTOP him REC marry“She’ll marry (with) him.”(4) * H  A d)

kanojyo wa kare to totsugu

In order to filter out inappropriate candidates, we compare the usage of SK with SU
using examples from a corpus. Two judgments are made for each paraphrase pair: is46

the paraphrase grammatical, and if it is grammatical, are the meanings similar?
This judgment can be done by monolingual speakers of the source language. We
test both directions: first we find example sentences using SU , replace SU with SK andcompare the paraphrased sentences. Then we find sentences for valence patterns using
SK , replace them with SU and judge the similarity. Figure 3.7 shows the comparison
using paraphrases.
For each candidate pattern SU -T (from SK -T )• Extract 5 sentences using SU from the corpus
For each sentence– Replace SU with SK– Classify the paraphrased sentence into 3 grammaticality classes
if the class is grammatical∗ Classify the semantic similarity into 6 classes• Extract 5 sentences using each pattern of SK from the corpus– Replace SK with SU– Test as above
Figure 3.7: Flow of Paraphrasing Check

The three grammaticality classes are: grammatical, ungrammatical, grammatical in some context.7 Semantic similarity was divided into the following classes:

Ñ2 odosu “threaten” and SK ³2 odosu “threaten”• close: SU  2d gushin-suru “report” and SK ê 2d joushin-suru “report”• [SU ] broader: SU { w2 tsukuri-dasu “create” and SK `é2d hatsumei• same: SU

suru “invent”
7 The analysts also rejected 7.9% of the example sentences as irrelevant. These were sentences wherethe verb did not actually appear, but that had been selected due to errors in the morphological analysis.

47• [SU ] narrower: SU
suru “marry”

S42d saikon-suru “remarry” and SK ^42d kekkon>F2d• different nuance: SU
oushuu-suru “expropriate” and SK
toriageru “confiscate” (SU is more formal than SK .)

d

:Ï$

1 ê+

p2d

• different: SUtachi-mukau “confront” and SK
hanronsuru “argue against” (their meanings overlap so they are classified into otherclasses in some context.)
Next, we give an example of the paraphrasing; for the unknown Japanese word SU
gushin-suru “report” we look at the existing word SK
joushin-suru“report” which exists in the valency dictionary, with the same English translation.

 2d

ê 2d

We extract 5 sentences from our corpus which use SK , for example (5; slightly
simplified here), and replace SK with SU (6).
(5)

C® D ,G ,A k ê 0 êæ % w8

Keiei
toppu ni kono koto ojoushin-shi, OK ga deta.
management top
DAT this thing ACC report,
ok NOM came-out“I reported this to the top management and they OKed it.”(6)C ® D ,G ,A k  0 êæ % w8
Keiei toppu ni kono koto o gushin-shi, OK ga deta.

Similarly, we extract 5 sentences from our corpus which use SU , for example (7;slightly simplified here), and replace SU with SK (8).
(7)

d$ k l( 2d »^H C A  08

bassoku oomoku-suru hitsuyou wa nai
togushin-shita.
penalty ACC increaseneed
TOP nothing QUOT reported.

“I reported that there is no need to make the penal regulations more severe.”(8)

d$ k l( 2d »^H C A ê 08

bassoku o omoku-suru hitsuyou wa nai to joushin-shita.

Both paraphrases (6) and (8) are grammatical and both pairs (5, 6) and (7, 8) haveclose meanings. This is done for all five sentences containing SU and then done inreverse for all 5 sentences matching the pattern for SK .
48

The strength of this paraphrasing method is that non-experts can make the judgments and there is supporting data for them. The weaknesses are that it requires example sentences and is labor intensive.
We also investigate checking the paraphrases using web-data.8 In this experiment
we replace the target word as above and then look for the paraphrased sentence: if itexists then the paraphrase is good. However, the average length of sentence we could
find to paraphrase is 19 words (38 characters). We therefore got so few hits (fewer than1%) that the test was practically useless.
Multilingual Check
Another possible filter on overgeneration is to use multiple languages as pivots (Bondet al., 2001; Paik et al., 2001; Fujita and Bond, 2004). Because we match the entire
translation in language X, there is no overgeneration due to complex verbs. When
plain dictionaries are available in multiple languages, then the criterion can be variedfurther — for example to use all dictionaries and select these words which have atleast one matching translation of X (we call this UNION) or to use all dictionaries andselect only those words which have matching translations in all languages (we call this
INTER).
In our experiment we used a Japanese-to-Chinese machine dictionary available inmachine readable form: J − C (Shogakukan and Peking Shomoinshokan, 1987); andtwo dictionaries available on-line: Wadoku Jiten — a Japanese-to-German dictionary
J − G (Apel, 2002); and Dico FJ — a Japanese-to-French dictionary J − F (Desperrier,2002). In Table 3.2, we show the number of patterns for each of the plain dictionaries
used in this thesis. Three of these dictionaries (EDICT, Wadoku Jiten and Dico FJ)
are available on-line, and are growing over time; the numbers given here are for theversions we used. Most bilingual entries lacked POS tags, so we matched on thesurface form of all entries, even though most are not verbs or adjectives.
We use the plain dictionaries in several ways. First, we only use the pairs of SU
and SK which have the same: (1) Chinese translation C (we call this strategy CN), (2)German translation G (we call this DE), (3) French translation F (we call this FR), then(4) have at least one matching translation in C, G and F (UNION), or finally (5) havematching translations in all of C, G and F (INTER).
8 Thanks to an anonymous reviewer for the suggestion.

49

Table 3.2: Size of J-X Dictionaries
J-X
Japanese
X
Pairs
J-C
72,400 102,300 180,800
J-G
252,400 224,000 526,000
J-F
16,600 10,500 37,900
J-E (EDICT)
94,200 80,400 154,600
J-E (ALT-J/E) 323,700 276,100 415,000

Japanese (SU )

R
matou

Pivot(TUK )chūan(Chinese)
wear(English)Japanese (SK )

½

Àd
kiru

.d
yowaruÕkñ$Rdwarai-o ukaberu
Figure 3.8: Creating Candidates through Multiple Pivots
An example of the utility of adding another language is shown in Figure 3.8. In
matou,
kiru,
yowaru andwarai-o ukaberu havethis case,the same English translation wear. But the word wear is polysemous and the Japanesepairsmatou andyowaru or
warai-o ukaberu don’t have similar meanings. Because Chinese verbs have different patterns of polysemy to English,
matou andkiru) is linked byonly the appropriate Japanese candidate pair (both English and Chinese (Figure 3.8).
If the source word itself is polysemous (or is monosemous with multiple subcategorization frames), then there can be more than one candidate language word linked
through multiple languages. In this case we will build multiple patterns. Each pattern
would correspond to a different sense.

R

R

Àd

.d

.d

Õkñ$RdÕkñ$Rd
RÀd

Association Scores
We also tested the use of association scores based on word-vector distances taken fromword-definitions and corpora (Kasahara et al., 1997). This measure is designed to50

simulate human word association.
First we used the association score to cut off subthreshold candidates. Then weused the score to rank words in order of similarity and only create patterns for words
judged similar by association. We investigated creating patterns for various ranks ofsimilarity: words more similar than a threshold, the most similar word, and words
that were within the top 10, 100, 1,000 and 10,000 most similar words. The strengthof this method is that it is fully automatic. The weakness is that highly associatedwords are not necessarily syntactically or semantically similar (for example
kekkon-suru “marry” andtotsugu “marry into”).

^42d

d)

Translation Link Strength
We also evaluated the quality of the English translation link. This was measured using
the dice coefficient. That is, if SU has English translations T (SU ), and they link throughthe valency dictionary to a Japanese word SK with translations T (SK ), then the strengthof the link is:(3.1) link strength =2 × (|T (SU ) ∩ T (SK )|)|T (SU )| + |T (SK )|This is similar to the one-time inverse consultation score used by Tanaka et al.
(1998) to link Japanese and French through English. The strength of this method isthat it is fully automatic. The weakness is that it depends entirely on the quality of thebilingual lexicon.

3.2.4 Making Candidates Robust
In order to make the system robust, we add alternative candidates and then merge
similar candidates.
Adding Alternative Patterns
If the pattern in the seed valency dictionary participates in a diathesis alternation (suchas I broke the cup ⇔ The cup broke), then we create candidates for both alternatives atonce.
chakka-suru “ignite” matches
For example, the unknown verb
inka-suru “ignite” which has two alternatives in the seed dictionary linked by the

d

Àm2d
51

Ûm2

Causative/Inchoative Alternation. We make patterns for both of them, allowing us tomatch both (9)9 and (10).
(9)¨m¿ % Àm 08

doukasen ga chakka-shita.
fuse
ACC ignited/caught fire

“The fuse ignited. / The fuse caught fire. ”(10) H ¨m¿ D Àm 08

kare wa doukasen ni chakka-shita.
DAT ignited.
He TOP fuse
“He ignited the fuse.”

This can only be done if the seed dictionary contains information about alternations. Currently, identifying alternations and adding them and to lexicons, is being
done both by linguists (Furumaki and Tanaka, 2003) and computational linguists (Dorr,
1997; Bond et al., 2002; McCarthy, 2000).
Merging Patterns
Merging similar candidates is an important problem for corpus-based approaches,
which normally have 10s to 1000s of candidates to merge (Li and Abe, 1998; McCarthy, 2000). In our case we have fewer candidates, and more information. Although
the existence of very similar patterns does not affect the translation quality, the redundancy creates spurious ambiguity, which slows the system down and makes debugging
harder.
We reduce the number of redundant patterns by merging similar patterns. First, if
two patterns are identical, we merge them. We then merge candidates that only differ
in their case-markers and selectional restrictions. That is, they have the same Japanese
head-word, the same English head-word, the same English subcat, the same numberof arguments, and the same case-roles. If the patterns have different case-markers,
the merged pattern is given the union of the two sets (for example if the argument of
SU1 has { } ni “to”, and the argument of SU2 has { , } ni,e “to”, then the merged
pattern will have { , } ni,e “to” as its case markers. However, if one of the similar

D

DQ

DQ

9 Actually, (9)’s translation is catch fire.

We use ignite only for explanation of the alternation in

English.

52

patterns is from a domain-specific dictionary, it is rejected in favor of the pattern fromthe general dictionary, rather than being merged.
We tested two strategies for merging selectional restrictions: parent and child. Allpairs of SRs from the two patterns are compared. In parent, if one restriction subsumes
the other the least restrictive (the parent) is used. In child, the most restrictive (thechild) is used. If neither restriction subsumes the other, then both are used. Multiple
patterns can be merged, not only pairs of similar patterns.
At this step, if the original pattern is marked in the transfer lexicon as a technicalterm and its lemma used in other candidate patterns, we don’t use the pattern. This
stops us from basing patterns on very specialized usages of words if we have otheralternatives.

3.3 Creation and Evaluation
In this Section, we create new patterns and apply several filtering methods (Step 1, 2
of Figure 3.5). These are then evaluated according to their effect on translation quality(Section 3.3.3) or by expert lexicographers (Section 3.3.4).

3.3.1 Target Verbs
We use the valency dictionary from the Japanese-to-English machine translation system ALT-J/E as a seed lexicon (See, Section 2.2).
In ALT-J/E’s Japanese-English word dictionary, there are 55,615 J-E pairs whose
Japanese part of speech is adjective, adjectival noun or verb. There are a total of
20,925 distinct Japanese entries. However, due to the cost of making detailed entries,
only those 4,937 entries have valency patterns: 15,988 entries have no pattern. Of
the 55,615 J-E pairs, 35,999 have no pattern in the valency dictionary. Our method isapplicable to 13,408 of these pairs: their English entry has an pattern in the valency
dictionary.
In Table 3.1 and Figure 3.2, we showed that 8,304 kinds of verbs have no pattern.
Of those, 4,129 (49.7 %) verbs appear in the ALT-J/E’s Japanese-English transfer dictionary or EDICT and have a pattern with the same translation in the valency dictionary(See Table 3.3).
53

Table 3.3: The Possibility of Increasing Cover Ratio for Japanese Newspapers (9 years)
In lexicon
No. of Types (%) No. of Tokens (%)
Japanese exists 4,997
37.5 24,656,590 92.5
English exists
4,129
31.0 1,355,552
5.1
No pattern
4,175
32.4
645,158
2.4
Total
13,301100.0 26,657,300 100.0

3.3.2 Results of Creation using Several Filtering Methods
We targeted the 4,129 verbs to create new patterns using multilingual check, association score and the link strength. Then, because we were able to find 5 or more examples
from a corpus of newspaper for 3,753 (90.9 %) of these, we targeted the 3,753 verbs
to test the paraphrasing filter and pre-filter. Table 3.4 shows the number of createdpatterns for the target verbs through the several filters.
The original number of candidates for the 3,753 target verbs was enormous: 108,733
pairs of SU and SK . Most of these were removed in the pre-filtering stage, leaving 2,492
unknown verbs matching 7,902 SK s in the valency dictionary. After the pre-filter, there
were on average 3.2 patterns/verb.
For the paraphrasing filter, analysts took about 7 minutes per verb. The data wassplit between three analysts, one a linguist and two people with no special training.
The other three filters (multilingual check, association scores and link check) are
fully automatic.

3.3.3 Translation Task-based Evaluation of Filtering Methods
In this section we evaluated the effect on translation quality for created patterns using
various filters. For each verb (SU ) we picked the two shortest sentences we could find(on average 81.8 characters/sentence: 40 words) from a corpus of 9 years of newspaper
text (4 years of Mainichi and 5 years of Nikkei)10 . This corpus had not been used in theparaphrasing filter, i.e., all the sentences were unknown. We tried to get 2 sentences
for each verb, but could only find one sentence for some verbs. For the pre-filter, the10 Mainichi ’93, ’96, ’97, ’98 and Nikkei ’90, ’91, ’92, ’93, ’94.

54

Table 3.4: Number of Created Valency Patterns

Filter
Pre-Filter
Paraphrase

Patterns Verbs Average
(P)
(V)
(P/V)

Filtering Methods
Condition
SU ⇒ SK : grammatical % ≥ 90
SK ⇒ SU : same or close % ≥ 90
CN

Multilingual
Check

DE
FR
INTER
UNION

Association 1st ranked
Scoresscore ≥ 0.8
score ≥ 0.7
Link Strength

7,902
321
2,716
2,077
7,826
629
141
9,178
2,161
89
273
4,814

2,492
205
1,428
668
90.7
8.2
51
1,868
1,632
55
163
778

3.2
1.6
1.9
3.1
4.6
4.1
2.8
4.9
1.3
1.6
1.7
6.2

number of target sentences is too large to evaluate them all, so we did an evaluation
over a sample.
We translated the test sentences both with the valency dictionary which has the
new patterns, and w/out the new patterns. When there is no pattern for a verb in thevalency dictionary, the system uses either the default translation in the plain dictionary
or if there is no entry in the plain dictionary, the Japanese verb as is.
Translations that were identical were marked no change. Translations that changed
were evaluated by people fluent in both languages. The evaluators were shown randomized translations to make the evaluation blind. The new translations were placed
into three categories: improved, equivalent and degraded. All the judgments werebased on the change in translation quality, not the absolute quality of the entire sentence.
For example, in (11) the change is evaluated as “B is improved compared to A”, inthis case, B is with, i.e., with is improved.

55

(11)

¥( [G % d A % C-Y[G 2

ugoku mono ga iru to kokoro ga nagomu mono desu.
move thing NOM exist if heart NOM calm down which is

A: If there is a thing which moves, a heart is softened.
B: If there is a thing which moves, we calm down.

The results of the evaluation for each filtering method are given in Table 3.5.
Thresholds were chosen after examining the data over a wide range of values, although
we do not show all the results here. In addition to the number of sentences which improved or degraded, Table 3.5 shows the difference (improved − degraded).
Table 3.5: Task-based Evaluation of New Patterns for each Filtering method

Filtering
Methods

Judgment of Translation Quality
improved no change equivalent degraded
No. % No.
% No.
% No.
%

Pre-Filter
32
26(Estimation)
Paraphrase11 1,636 37.5 1,063 24.3 1,115
Multi- CN 305 23.5 392 30.2 410
lingual DE 776 24.7 991 31.6 809
39 16.6
98 41.770
Check FRUNION 873 24.2 1,153 31.9 950
Association
18 15.8
47 41.2
28
Score ≥ 0.8
Link Strength 366 22.1 510 30.8 422
≥ 0.9

26
25.5 552
31.6 192
25.8 561
29.8 28
26.3 634
24.6 21

difference
Total
No.
% No.

16

+16

12.6 1,084 +24.9 4,366
14.8 113 +8.7 1,299
17.9 215 +6.8 3,137
11.9
11 +4.7 235
17.6 239 +6.6 3,610
18.4-3 -2.6 114

25.5 359 21.7

7 +0.4 1,657

As can be seen in Table 3.5, paraphrasing gives the best quality; that is using only
the pre-filter and the grammaticality judgments, 37.5% of translations improved andonly 12.6% degraded, an overall difference of +24.9%. Pre-filtering gives the second best quality (estimated quality). The difference using a pre-filter is +16%, which
is a good result.
11 We use the patterns that have at least one S to S paraphrase that is grammatical.
UK

56

In the results of multilingual check of Table 3.5, the overall difference ranges from
+4.7% to +8.7%. The biggest improvement was for CN, which comes from a totallydifferent language family to English. In all cases, the number of improved sentences isgreater than those degraded, but UNION creates the most patterns, and has an overall
difference of +6.6%.
Table 3.5 shows that the scores from association and link strength are not high.
Therefore, we conclude that association scores and link strength are not suitable filtersfor calculating syntactic or semantic similarity in this task.
In summary, paraphrasing gives the best quality of translation, but pre-filtering ischeaper and satisfies both quantity and quality. Of the fully automatic methods, themultilingual check using UNION gives the best results. The translation results areanalyzed in more detail in Section 3.4.1.

3.3.4 Lexicographers’ Evaluation of Filtering Methods
In this section we evaluate the effectiveness of the filters, using direct analysis byexpert lexicographers as our gold standard. The results of several methods are given in
Table 3.6.
In Table 3.6, precision is the percentage of acceptable patterns that passed the filter
over all patterns that passed the filter. Recall is the percentage of acceptable patterns
that passed the filter over all acceptable patterns12 . The baseline is to use all patterns
that passed the pre-filter: this gives a precision of 53.4% and 100% recall.
The highest precision (72.3%) came from only using patterns where the unknown
verb (SU ) was the most similar to the known verb (SK ). The recall, however is adisappointing 3%. Using the paraphrase tests based on sentences where the unknown
verb replaced the known verb, gave almost as high a precision and a higher recall(71.8% and 23.7% respectively).
Next we considered the multilingual filter. Using one dictionary (the strategies;
CN, DE and FR), DE gives the highest recall, but precision is not so high. CN gives
11.4% recall, but its precision is higher than DE. This can be explaned by the following:(1) because the Japanese-to-German dictionary is larger than the Japanese-to-Chinese
dictionary, the Japanese-to-German dictionary has more polysemy and the polysemy
12 The number of patterns that passed pre-filter is enormous, so we evaluated a sample set.

sample set, the total number of acceptable patterns is 4,272.

57

In thisTable 3.6: Lexicographers’ Evaluation of New Patterns for each Filtering method

FilterPrecision Recall F-score
(%)
(%)
(%)

Filtering Methods
Condition

Pre-filter

ParaphrageSU ⇒ SK : grammatical % ≥ 90
SU ⇒ SK : ungrammatical % = 0
SU ⇒ SK : same or close % ≥ 90
SK ⇒ SU : grammatical % ≥ 90
SK ⇒ SU : ungrammatical % = 0
SK ⇒ SU : same or close % ≥ 90
CN

Multilingual
Check

DE
FR
INTER
UNION

Association Score 1st ranked
Link Strength
score ≥ 0.9

53.4 100.0
57.1 61.0
57.1 61.3
70.2 22.0
61.7 55.1
61.7 55.7
71.8 23.7
66.9
3.2
63.2 11.4
64.1
0.9
73.8
0.2
62.3 13.2
72.3
3.0
59.6
7.1

53.5
59.0
59.1
33.5
58.2
58.5
35.6
6.1
19.3
1.8
0.4
21.8
5.8
12.7

makes the accuracy low. (2) German and English are in the same Germanic groupof Indo-European language family, but Chinese and English are in different language
families. So, Chinese is more effective for filtering-out the wrong pairs caused by
English polysemy.
Even so, the most forgiving method, UNION gives good precision, and its recall ishigher than the remainder. So UNION is the most useful of the multilingual filtering
strategies.
These results show the same trends as the task-based evaluation: Paraphrasing
gives the highest score, the pre-filter is next, and the multilingual pivot using UNION
is the best of the fully automatic filters. We discuss the results of the lexicographers’
evaluation in more detail in Section 3.4.2.
58

3.3.5 Evaluation of Alternations and Merging
In this section, we evaluate the methods used to make the new patterns robust (Step 3 ofFigure 3.5). For this evaluation, we take the patterns which passed the pre-filter (thosewhich satisfied both quantity and quality, and including the patterns made throughparaphrasing) and we use them as a basic set of new patterns. We add some alternative
patterns, and then merge any similar patterns. Then, analysts evaluate the created,added and merged new patterns.
An additional 178 patterns were made using alternations. At the next step, we wereable to merge 2,891 similar patterns into 1,183, leaving 6,327 candidate patterns for
2,492 verbs. The maximum number of patterns merged into one was nine (kanchigai-suru “mistake”). Half the mergers used the parent method and half usedthe child method Section 3.2.4. Table 3.7 shows the number of patterns which had
case-markers (CM) or SR merged. In Table 3.7, 50% of the patterns had CMs merged
and over 97% had SRs merged.

Ä2

d

Table 3.7: Number of Merged Patterns

Both Merged
only SR Merged
Same
Total

parent
child
Total
No.
% No.
%
No.
%
324 54.8 311 52.5
635 53.7
254 43.0 268 45.3
522 44.1
13 2.2 13 2.2
26 2.2591 100 592 100 1,183 100

After merging, there were 2.5 patterns/verb, a much closer ratio to that of the seed
lexicon.
The results of the analysis are given in Table 3.8. Separate columns are shown forpatterns made using alternations, patterns that were merged using the parent method,patterns that were merged using the child method, the remainder of the patterns andall the patterns. These results are used to evaluate the similarity filters.
The evaluation took around 5 minutes per verb. Each pattern was marked as:
acceptable, fixable or useless: acceptable patterns could be used as they were;
fixable patterns could be used with minor revisions; useless patterns were so poor
59

Table 3.8: Lexicographers’ Evaluations for New Patterns
Alternation
ResultNo.
%
Acceptable 53 30.8
Fixable63 36.6
56 32.6
Useless00
Total
172 100

Merge
ParentChild
Remainder
Total
No.
% No.
%
No.
%
No.
%
366 61.9 333 56.3 2,505 50.4 3,257 51.5
195 33.0 231 39.0 1,803 36.3 2,292 36.2
28 4.7 26 4.4
640 12.9
750 11.9
2 0.3
2 0.3
24 0.528 0.4591 100 592 100 4,972 100 6,327 100

that it would be easier to create a pattern from scratch.
The majority of patterns that passed the pre-filter were usable as they were (51.5%).
A further 36.2% were usable with minor revisions, giving 87.7% potentially usefulpatterns. These are encouraging results.
Patterns made using the alternations were worse overall, while those made by merging were substantially better. One of the reasons for the poor quality of the alternations
is that they added another transformation to the original. If we consider only alternations of acceptable patterns, then they are acceptable 30.8% of the time. Therefore, it
is better to make patterns using alternations after all other filters have been applied.
Fewer fixes were necessary for the patterns merged with more general restrictions(parent:child — 61.9%:56.3%) than with the more restricted patterns, although bothwere better than the remainder.
Examining the kinds of changes needed by the merged patterns showed the childset needed their SRs corrected more often. This shows clearly that merging to the least
restrictive values (the parent strategy) is the best.

3.4 Discussion
In this section, we analyze the results of translation evaluation (Section 3.3.3) anddirect evaluation (Section 3.3.4) in more detail. Then, based on the results of theanalyses, we refine our proposed method.
60

3.4.1 Analysis of the Translation Results
First, we analyze the reasons for the improved and degraded translations.
Reasons for improved results: (1) The system was able to translate previously
unknown words. The translation may not be the best but it is better than an unknown
word. (2) A new pattern with a better translation was selected. (3) The sentence wastranslated using the correct subcategorization, which allowed a zero pronoun to besupplemented or some other improvement.
We show some examples of the changed translations, using simplified example
sentences.
In (12) the English valency information supplies the subcategorized preposition forin wish for. The default translation makes the argument a plain direct object, which isungrammatical for wish.
(12)Ú G do % , k f0 6G 8Z G F k ø Ë¸ %kokumin no taihan ga heiwa o hosshi, sono tame no kiken o ou kakugo ga
d A 1? &8
aru to shinjite kita.

w/out: It was believed that national most wished peace andthat there was the preparedness that we owe danger for that
purpose to.
with: It was believed that national most wished for peace andthat there was the preparedness that we owe danger for that
purpose to.
In (13), the translation with is an improvement.
(13)éÜïê H Âªy0 D ¬° k ê08
NATO

ha Serbia-jin ni sensen o fukoku-shita.

w/out: NATO decreed a declaration of war to Serbia person.
with: NATO announced a declaration of war to Serbia person.
Reasons for degraded results. (1) A new pattern was selected whose translation
nadewas less appropriate. (2) the detailed nuance was lost. For example,
ageru “brush up” became simply brush.
The main reason for these degradations was a change in the default translation.
When there is no pattern available, ALT-J/E uses a translation from its word dictionary.

þê+d

61

As there is little information available to choose between alternatives, the first listedtranslation is used (the first listed translation is meant to be the most general translation). However, when we made patterns, we looked at all listed translation equivalents.
51% of the time we were able to make a pattern with the first listed translation, 27%
with the second, 11% with the third, and 11% with the fourth, fifth or sixth. However,when a pattern exists ALT-J/E uses it in preference to an entry in the word dictionary. Therefore, the translation was changed for many patterns. Sometimes the newtranslation was an improvement, but sometimes it was not. For example,
kuchi-gotae-suru “answer back” had two translations in the word dictionary: (1) answer back and (2) retort. We could only find a pattern for retort and so this became thesystem’s choice. However, answer back was in fact a better translation in the examples
we saw.

Î!2d

3.4.2 Analysis of the Lexicographers Evaluation
Direct evaluation shows two things that the task-based evaluation did not make clear.
The first is the utility of merging similar patterns: the resulting patterns are of highquality, and the dictionary becomes more compact. When merging, the best strategy is
to create new patterns with less restrictive selectional restrictions. The second is that
evaluation by paraphrasing is no better than using expert lexicographers. Althoughusing paraphrasing does improve the quality of the dictionary, it is quicker and moreaccurate to use lexicographers directly (5 minutes vs 7 minutes). Further, paraphrase
judgments are hard to make for untrained analysts: linguists made paraphrase judgments with higher accuracy.
For example in (14), (15), the meaning is changed in (15) but if we assume thespecial state, (15) will be acceptable. This falsifies the claim that paraphrase judgments can be done cheaply with untrained analysts, and makes it less effective to use
paraphrasing as a filter.
(14)ËxÁæW

% Ó08

öL

Vaccari-fusaiga arawa-shita waei-jiten.
Mr. and Mrs. Vaccari ACC wrote
Japanese-English dictionary.

“The Japanese-English dictionary is written by Mr. and Mrs. Vaccari.”62ËxÁæW % Ìd08 öL(15) ?
Vaccari-fusaiga hyouki-shita waei-jiten.

“The Japanese-English dictionary is signed on the front by Mr. and Mrs. Vaccari.”

From a practical point of view the results are encouraging: we can produce useful
new patterns with only a simple monolingual judgment as pre-filter: “are these verbs
similar in meaning?”, and it has been shown that these patterns improve the quality oftranslation in 32% of sentences versus degradations in only 16%.
The quality can further be improved by the candidates being checked by lexicographers. This is relatively expensive, at an additional 5 minutes per verb, but is stillcheaper than creating patterns from scratch. Preliminary investigation shows that evencorrecting the fixable patterns takes less than 10 additional minutes per pattern on average, for a total of 15 minutes per pattern.
At the end of these experiments, we increased the valency type coverage about 1.5
times (from 4,997 to 7,427) and cover almost half of the missing tokens. This means
in practice that the number of sentences which have unknown verbs decrease from one
in 5 to one in 9 using the data from 9 years’ newspapers (see Table 3.9 and Figure 3.9:
we graphed Table 3.9 as Figure 3.9).
Overall, our results show that hand-compilation is still necessary for building highquality lexicons. However, semi-automatic acquisition of candidates, and merging theacquired candidates can increase efficiency considerably.

3.4.3 Refining the Method
As we showed in Section 3.4.2, the method for creating new patterns using only a prefilter is very effective. The pre-filtering is a very simple judgement, done by people.
So, to reduce the cost, we examine whether we can use automatic filters instead ofhuman pre-filters, even if only for some of the target words.
Table 3.6 showed that the most efffective automatic filter was the Multilingual
check, used after a pre-filter. So, we ran the multilingual check without applying a
pre-filter. The results for Table 3.6 are shown after applying the pre-filter. Here, we
consider doing the multilingual check without applying the pre-filter.
63

Table 3.9: Cover Ratio of Created Patterns for Japanese Newspapers (9 years)
In lexicon
No. of Types (%) No. of Tokens (%)
Japanese exists
4,997
37.5 24,656,590 92.5
Created Japanese exists 2,430
18.3
886,126
3.3
No pattern
5,874
44.2 1,114,584
4.2
Total
13,301100.0 26,657,300 100.0

Figure 3.9: Graph of Cover Ratio of Created Patterns for Japanese Newspapers (9years)

64

Table 3.10 shows the results using the multilingual check without applying a prefilter. The Japanese-to-French dictionary is relatively small so in Table 3.10, we use
Japanese-to-Chinese (J − C) and Japanese-to-German (J − G) dictionaries. It meansin Table 3.10, UNION has at least one matching translation in C and G, and INTER
has matching translations in both of C, and G. Table 3.10 shows the number of target
verb pairs whose SU and SK both exist in the dictionary, and the number after applying
pre-filter, too.



Table 3.10: Number of Creatable Valency Patterns Using Multilingual Check
Without Pre-filter(T13 ) (P14 ) (V15 )Filtering Methods
FilterCondition

Through Pre-filter
(T)
(P)
(V)

No Filter

108,733
7,902 2,492
CN
SU , SK exist in J −C 26,715
9,540
1,474 2,077
890
938 1,389
545
CU =CK
DE
SU , SK exist in J − G 91,357
31,3896,178 7,826 2,892 3,803 4,944 1,631
GU =GK
UNION SU , SK exist
92,6289,540
CU =CK or GU =GK
6,981 8,933 2,729 4,245 5,594 1,592
INTER SU , SK exist
25,444
9,053
CU =CK , GU =GK
671
970
470
496
739
323

From Table 3.10, 470 patterns for 671 pairs are creatable if we use INTER, butof those, only 496 pairs (73.9%) went through the pre-filter. We checked 34 pairs(19.4%) of the remaining 175 pairs which are not through the pre-filter. The 34 pairs
can expand to 45 patterns. Of those, 37 patterns (82.2%) should be acceptable.

/dmetoru “marry” and SK
morau “marry” wasFor example, the pair of SU
rejected in the pre-filter, but it has same Chinese and German translation. From thismorau “marry” we can create an acceptable new pattern for SU
pattern of SK
metoru “marry”. Now,
morau has polysemy. From the Gakken Japanese
Dictionary (Kindaichi and Ikeda, 1988),
morau has 6 senses: That is (1) given,

/d







65

(2) get, (3) marry, (4) have a break, (5) take on, and (6) win.
At the pre-filter stage, it is likely that the less familiar meanings of the word16 were
not considered by the analyst. The multilingual check can not only reduce the cost, butalso cover the mistakes due to human error. Of course, not all the patterns produced
through the multilingual check are correct, but doing the check before the pre-filter iscost effective.
The multilingual check is useful, but only 23.2% of the target pairs are seen withboth SU and SK in the Japanese-to-Chinese dictionary. Even in the Japanese-to-German
dictionary, only 79.3% of the target pairs are seen. So, we should use the pre-filter forthe remaining patterns.
We therefore propose a method of building information-rich lexicons that proceeds
as follows: (1) build a seed lexicon by hand; (2) extend it automatically using morethan one bilingual lexicon; (3) extend it semi-automatically using bilingual lexicons
and a simple pre-filter check; (4) merge any similar patterns, making the selectional
restrictions broader rather than narrower; (5) revise the new patterns as far as possible.
This method is also applicable to work in new language pairs. It will always be
the case that simple bilingual lexicons are larger than information-rich lexicons —therefore it will be worthwhile using the former to extend the latter.
Our work is similar in spirit to that of Dorr et al. (2002), who link two informationrich resources (one English and one Chinese) using a bilingual dictionary. They thenuse the bilingual dictionary to fill in gaps, effectively using a simpler resource to increase the size of the information-rich lexicons.
Kanamaru et al. (2005) examined a method to get Japanese frames using the English FrameNet (Johnson et al., 2002) and an English-Japanese bilingual corpus. They
found candidate Lexical Units via the manually translated words. The method has only
osou “attack”. This method can also be used tobeen evaluated for a single verb:
provide a bilingual valency dictionary, using a well aligned bilingual corpus instead ofa plain dictionary.
An earlier version of this work (Fujita and Bond, 2002a) inspired Hong et al. (2004)

X

10 T is the No. of Target Verb Pairs.
11 P is the No. of Patterns.
12 V is the No. of Verb Types.
16 The pre-filter rejected patterns based on senses 3 and 6: that is, pairs with S

and SU

A2d hakusuru “win”.

66

K/d metoru “marry”

to use the same method to create Korean-Chinese patterns, extending the number ofpatterns in their pattern-based machine translation system from around 110,000 to
350,000. They used three automatic checks: (1) the verbs must have the same voice;(2) neither verb must be an idiom and (3) the target language verb cannot be a light verb
(support verb). The automatically created verbs were then checked by a lexicographer
and non-synonyms rejected (pre-filter). The newly created verbs raised the percentageof perfectly matched patterns from 59.2% to 64.4% a gain of 5.2%. This shows thatthe general approach is fully extensible: it works for different systems and for different
language pairs.

Future WorkWe would like to experiment with more aggressive merging. In this thesis, we only
merged patterns with the same case-roles and same English subcat Section 3.2.4. But
when the case-roles differ only with adjunct case-slots, we could potentially merge
them.

2<Je(

suppanuku “expose” had the following two candidate
For example,
patterns as shown in Figure 3.10 and 3.11.

 N1 h3:agenti% ga N2 h1236:human-activities, ...i k o N3 h3:agenti
D ni
 2<Je( suppanuku “expose”
Figure 3.10: Candidate Pattern for 2<Je( suppanuku “expose” (1) N1 h3:agenti% ga N2 h1236:human-activities, ...i k o 2<Je( suppanuku “expose”
Figure 3.11: Candidate Pattern for 2<Je( suppanuku “expose” (2)They have different case-roles, but the N3+ni of Figure 3.10 is an adjunct casemarker, and the two patterns should be merged.
67

3.5 Conclusion
In this thesis we present a method of assigning valency information and selectional restrictions to entries in a bilingual dictionary. The method exploits existing dictionaries
and is based on two basic assumptions: words with similar meaning have similar subcategorization frames and selectional restrictions; and words with the same translations
have similar meanings.
A prototype system allowed 6,327 new patterns to be built, using only simple human judgement (pre-filter). Of those more than 51% were usable as is, and more than
36% were usable with minor revisions, giving 87.7% potentially useful patterns. The
cost, including human revisions, is less than 6 minutes per pattern. Furthermore, even
before applying human revisions, adding the created patterns to a Japanese-to-English
machine translation system improved the translation for 32% of sentences using theseverbs, and degraded it for only 16%, a substantial improvement in quality.

68Chapter 4
Acquisition of Valency Entries using
Alternation Data
In this chapter, we present a method that uses alternation data to add new entries to an
existing lexicon. If the existing lexicon has only one half of the alternation, then our
method constructs the other half. The new entries have detailed information about argument structure and selectional restrictions. We also show that it is possible to simultaneously add entries in two languages if your exisiting lexicon has such information.
In this section we focus on one class of alternations, but our method is applicable toany alternation1 .

4.1 Introduction
In this chapter we propose a method of acquiring detailed information about predicates,
including argument structure, semantic restrictions on the arguments and translationequivalents. It combines two heterogeneous knowledge sources: a seed lexicon, andinformation about verbal alternations. Ultimately, we will use the method with a rangeof alternations, however, as a proof-of-concept in this section, we consider transitive
alternations where the object of the transitive is the same as the subject of the intransitive (e.g. the acid dissolved the metal ⇔ the metal dissolved (in the acid)) (Levin,
1993, 26–33). The algorithm can, however, be extended to other alternations.
1 First we reported in Fujita and Bond (2004a), then revised in a journal, Fujita and Bond (2005).

69

We focus on acquiring Japanese verbs, using the valency (pattern) dictionary fromthe Japanese-to-English Machine Translation System ALT-J/E as our seed lexicon (See
Section 2.1, 2.2). Using this, we actually create Japanese and English entries at thesame time.

4.2 Alternations
Most verbs have more then one possible argument structure (subcat). These can be regularized into pairs of alternations, where two argument structures link similar semanticroles into different subcats. Over 80 alternation types have been identified for English(Levin, 1993). However, in this section we will only be considering those between
transitive and intransitive uses of verbs, where the subject of the intransitive verb (S)is the same as the object of the intransitive verb (O). We will call the subject of thetransitive verb A (absolutive).
In order to compare English and Japanese alternations, we compiled a list of 449
Japanese verbs that took transitive/intransitive alternations, based on data from Jacobsen (1981), Bullock (1999) and the Japanese/English dictionary EDICT (Breen, 1995).
Japanese, unlike English, typically morphologicaly marks the transitivity alternation.
A typical pair is given in (16) (See Figure 4.2 for more detail including both the subcategorization frame and the selectional restrictions).
(16)

Vi

Vt

V*d S tokeru ⇔ V( A O toku
S dissolve⇔ A dissolve O

To contrast the Japanese with English, we also investigated the English translationsof the Japanese S = O transitive pairs. Many verbs had multiple translation equivalents,
there were 839 Japanese-English pairs in all. The classification of the English types isgiven in Table 4.1.
We divide the entries into five classes. The first three are those where the main
English verb is the same. The most common class (30%) is those where the English
verb also allows the S = O transitive alternation. The next most common (20%) isentries where the Japanese intransitive verb can be translated by making the transitive
verb’s translation passive: A omit O/S be omitted. In the third class (6%) the English
70

Table 4.1: Classification of English Alternation
Japanese
Vi

Vt

.Wd

.Zd

öed

öb2

(

$2

(Cd (21\ed 1\b2English Translation(Structure)
ViVt
S weaken
A weaken O( S ViA Vt O )S be omitted A omit O( S be Vt-ed A Vt O )S cry
A make O cry
S Vi/be Adj
A Vc O Vi/Adj
S pass away A lose O( S ViA Vt O )S playA play with O( S ViA Vt prep O )

Type

No.

S=O

138 30.0

passive

91 19.8

synthetic30—

197 42.8

—

4

(%)

6.5

0.9

Vc is control verb such as make,get,let,become.
Many entries also include information about non-core arguments/adjuncts.

is made transitive synthetically: a control verb (normally make) takes an intransitive
verb or adjective as complement: S cry/A make O cry. The last two are those where
either different translations are needed (44%), or the same English verb is used but thevalency change is not one of those described above: S play/A play with O. We show
the details of this classification results in Appendix B.
From the point of view of constructing lexical entries, if the English main verb stays
the same, then we can automatically construct a usable English translation equivalent
along with the Japanese alternation. This should be possible 56.3% of the time. There
are two caveats. The first is that the translation may not be the best one, most verbs
can have multiple translations, and we are only creating one. The second is that thisupper limit is almost certainly too low. For many of the alternations, although our tablecontained different verbs, translations using identical ones could also be constructed.
71

4.3 Comparing Selectional Restrictions of A, O and S
In alternations, a given semantic role can appear in two different syntactic positions:for example, the DISSOLVED role is the subject of intransitive dissolve and the objectof the transitive. Baldwin et al. (1999) hypothesized that selectional restrictions (SRs)stay constant in the different syntactic positions. Dorr (1997), who generates bothalternations from a single underlying representation also seems to make this assumption. Kilgarriff (1993), on the other hand, specifically makes the subject h+sentient,+volitioni, while the object is h+changes-state, +causally affectedi. In thissection we attempt an empiric approach and measure the differences by examining thesemantic classes used as SR of A, O and S of verbs in the S = O alternation.
Our source of data for the selectional restrictions is ALT-J/E’s valency (pattern) dictionary (Section 2.2). It consists of linked pairs of Japanese and English verbs. Bothverbs have information about the argument structure (subcat) of the verb. In addition
to the core arguments, adjunct cases are added to many patterns, to help in disambiguation (for more details, see Section 2.2). This is common in large NLP lexicons,such as COMLEX (Grishman et al., 1998), but rarely considered by linguists.2 TheJapanese side has selectional restrictions (SR) on the arguments. The arguments arelinked between the two languages using case-roles.
Each English entry is separated into the skeleton, which gives the argument structure, and the flesh, which adds the predicate and any fixed arguments (such as prepositions, particles, and nouns in multiword expressions like kick the bucket (Yokoo et al.,1994)). There are 616 different skeletons, with the most common ten (A Vt O , S be
Adj, . . . ) covering 72% of the entries. We show simplified examples of entries in
Figures 4.2 and 4.3.3 The flesh is shown underlined.
Bond et al. (2002) have previously identified alternation pairs between entries inthe dictionary. Of those links, there were 449 pairs where S = O. The SRs take theform of a list of semantic classes, strings or *, which matches anything. The semantic
classes are from the Goi-Taikei ontology of 2,710 categories (Ikehara et al., 1997). It
is an unbalanced hierarchy with a maximum depth of 12 (Level 11). The top node
(Depth 1, Level 0) is h1:nouni. Depth 12 (Level 11) includes h1960:cultivationi,
2 For example, the COMLEX 3.0 entry for surprised notes that it coocurs with about and at.
3 Actually, each entry has the same information with Figures 3.3 and 3.4.

72h1993:appearancei and so on. The lower the level, the more specialized the meaning,
and thus the more restrictive the SR. Because * matches anything, even non-nouns,
it’s the loosest restriction. Strings, which only match specific words, are the strictest
restrictions.
Figure 4.1 shows the distribution of the depth of the semantic restrictions for the
A, O and S arguments.

㪧㪼㫉㪺㪼㫅㫋㪸㪾㪼㩷㫆㪽㩷㩷㪪㪼㫄㪸㫅㫋㫀㪺㪚㫃㪸㫊㫊㪼㫊

㪌㪇㩼           

㪋㪇㩼
㪊㪇㩼
㪉㪇㩼
㪈㪇㩼
㪇㩼

㪁 㪈 㪉 㪊 㪋 㪌 㪍 㪎 㪏 㪐 㪈㪇 㪈㪈㪮㫆㫉㪻㪛㪼㫇㫋㪿㩷㫆㪽㩷㪪㪼㫄㪸㫅㫋㫀㪺㪚㫃㪸㫊㫊㪼㫊

Figure 4.1: The Level of Semantic Classes

The absolute arguments (A) have the loosest restrictions. The most common depth
is level 2, which includes h3:agenti and h2:concretei. The subject (S) and object(O) arguments show similar distributions, although O tends to be slightly more restrivtive.
This difference between A and the other two arguments was expected. SRs areused to distinguish senses.4 In an intransitive verb, with only one argument, its SRs
must have all the discrimination, and so should be relatively deep. In the transitive
verb, when the object has deep semantic restrictions, the subject is not so important as
a discriminator.
4 In the Goi-Taikei,

not just to disambiguate the Japanese sense, but also to choose the English

translation.

73

In the Goi-Taikei hierarchy, semantic classes subsumed by h3:agenti are h+sentient, +volitioni. A was very agentitive, with 58.9% of the SRs being subsumed by
h3:agenti. S is slightly agentitive (22.4%) and O is the least agentitive.
In summary, the distribution of SRs is similar for the same semantic roles, even in
the different grammatical positions of S and O. They are not, however, identical. In
particular, S is more agentitive than O.

4.4 Method of Creating Valency Entries
In this section we describe how we create new entries. Our resources are (1) a seed
lexicon of high quality hand-made entries; and (2) a list of verbal alternations. Our
strategy is to look for verbs which participate in an alternation, but for which an entry
exists for only one alternative. We then build the other alternate by a process of analogy
with the known entries which participate in this alternation.

4.4.1 Target
In this experiment, we only look at one family of alternations, the S = O alternation.
The candidate words are thus intransitive verbs with no transitive alternate, or transitive
entries with no intransitive alternate. Alternations should be between entries, but thealternation list is of words. Many of the candidate words (those that have a entry for
only one alternate) have several entries. Only some are suitable as seeds. We don’t use
entries which are intransitive lemmas but have an accusitive argument or which haveboth topic and nominative, such as (17).

H

´

% e*d

(17) N1:h4:peoplei
N3:h" "iha N3:”chikara” ga nukeru
N1
N1
TOP N3:power
NOM lose“N1 lose N1’s energy”
There are 129 entries (25 lemmas) which have only intransitive entries, and 84
entries (40 lemmas) which have only transitive entries. We create intransitive entries
using the existing transitive entries, and transitive entries using the existing intransitive
entries.
74

PID : 302116
S
N1:h533:objectsi ga
N3:h533:objectsi niVitokeru “dissolve”



 V*d

%
D

 N1 SUBJECT Vi dissolve
 PP in N3

S

PID : 508661
N1:h4:people, 760:artifactiAO
N2 h533:objectsi o
N3:h706:inanimatei niVitoku “dissolve”




 V(

% ga

 N1 SUBJECTk Vt dissolve
D
O  N2 DIRECT OBJECT PP in N3
Figure 4.2: Existing Entries (which undergo the S = O alternation): V( toku “dissolve” ⇔V*d tokeru “dissolve”
PID : 202204
S
N1:h3:agent, 535:animali ga
N3:h*i niViodoroku “be surprised”



D
 Ø(

%

New Entry’s PID : 760038
N1 h*i ga
AO
N2 h3:agent, 535:animali oVtodorokasu “surprise”


%

 Ø$2

A

S

 N1 SUBJECT Cop be Adj surprised
 PP at/by N3

 N1 SUBJECTk Vc makeO  N2 DIRECT OBJECT Adj surprised
Figure 4.3: Seed: Ø( odoroku “be surprised” ⇒ New entry Ø$2 odorokasu “surprise”

75

A

4.4.2 Creating the Japanese subcat and SRs
In creating the intransitive entries from the transitive entries, we map the O’s SRs onto
the S’s SRs, and change the case marker from accusitive to nominative. We delete the
A argument, and transfer any other arguments as they are.
To create the transitive entries, we map the intransitive S’s SRs onto the new O’s
SRs, and give it an accusitive case-marker. Then we add a causitive argument as absolutive subject (A) with a default SR of h3:agenti and a nominative case-marker(h3:agenti is the most frequent SR for transitive verbs undergoing this alternation).
If the intransitive entry has a demoted subject argument (where the Japanese casemarker is ni and the English preposition is by), We promote it to subject and use its SR
instead of the default. We show this entry in Figure 4.3. The we use the same othercase-frames as in the transitive entries.

4.4.3 Creating English Side
There are basically four choices for the English side: For the translation of a Japanesetransitive, the English can be transitive (Vt), or an adjective/intransitive verb embedded
in a control verb (Vc = synthetic): A make O cry. The intransitive side can be an
intransitive verb (or adjective) (Vi), or a passive transitive (Vp = passive).
To creat an intransitive entry from a transitive, we see if the original translation wasof type Vc or Vt. If it was Vc, then the complement of the control verb becomes thehead of the new entry.5
If the transitive entry was Vt, then, if the English verb undergoes the S = O alternation,
create an entry headed by an intransitive verb, otherwise passivize the verb. These
operations are summarized in Figure 4.4. To judge whether an English verb could
undergo the S = O alternation, we used the over-simple test that both transitive andintransitive entries appeared in our seed lexicon. This is reversed to make the transitive
entries.
In the implementation, this process was made complicated by the presence of various extra arguments. Many adjuncts such as source or goal, were included in the seed
lexicon to aid in selecting translations. In addition, many Japanese verbs were trans5 We made a special rule for the English Vt have.

There is: for example,

In this case the intransitive alternation will be

5U26A have O on X ⇒5O6 There be S on X
76

Creating Intransitive entries:• if the original subcat has a control verb (make,have,get,cause)– A Vc O Vi/Adj ⇒ S Vi/be Adj• else (original head is Vt)– if the transitive head undergoes the S = O alternation∗ A Vt O ⇒ S Vi
– else∗ A Vt O ⇒ S be Vt-ed
Creating Transitive Entries
If the original subcat is:• S Vi– if the intransitive head undergoes the S = O alternation
S Vi ⇒ A Vt O
– else ⇒ A Vc O Vi
• S be Adj ⇒ A Vc O Adj• S be Vt-ed ⇒ A Vt O
• S Vt ⇒ A Vt O
In this case, we use make as a control verb, Vc

Figure 4.4: Method of Creating the English Side77

lated as verb-particle constructions or other multiword expressions. In cases were there
was more than one candidate skeleton that could be used in the new entries, the mostfrequently used one in the known alternation examples was used. If there was a choice
betwen unseen skeletons, the most frquent overall was chosen. Finally, if an skeleton
could not be found automatically, a default entry was used, with the expectation that
it would need to be hand corrected. The defaults were: S Vi or S be Vt-ed/Adj (for the
intransitive side); A Vt O (for the transitive side).

4.4.4 Evaluation
A total of 213 new entries were created for 65 verbs using the method outlined in
Section 4.4. The quality was evaluated by expert lexicographers familiar with the seed
lexicon. The evaluation was divided into two steps: (1) a decision as to whether or not
the Japanese subcat and SRs gave a possible entry or not; (2) for the possible entries,
how much hand correction was needed to make a correct entry.

4.4.5 Evaluation: Entry Possible/Impossible
The results of the judgement as to whether the Japanese subcat and SRs gave a possible
entry or not are given in Table 4.2. A majority, (65%) were possible. Looking at theresults per verb (recalling that one verb can have multiple entries), there was at least
one possible entry for every verb. 69% of the verbs had at least one entry that was
usable as is.
Table 4.2: Is the Japanese Expression possible?

Created
Vi
Vt
Total

Possible Impossible
No.
% No.
%
45 53.6 39 46.4
93 72.1 36 27.9
138 64.8 75 35.2

Total
Entries (Verbs)
84
(25)
129
(40)
213
(65)

An example of an impossible entry is (18). The expression“be caught” is possible, but not with the semantic restriction
78

Nbhedtorawareru

h2:concrete, 2306:material-phenomenoni on the subject and the adjunct caseshown. Another entry created for torawareru (19) was judged to be possible. We
discuss in more detail in Section 4.5.1.
(18)∗ N1:h2:concrete,

2306:material-phenomenoni% Nbhedga

torawareru
NOM be picked up

N1
N1“ N1 be caught”% Nbhed(19) N1:h4:people, 535:animal, 760:artifacti
N1
ga torawareru
N1
NOM be caught“N1 be caught”

4.4.6 Evaluation: Fine Tuning
For entries where the basic Japanese structure was close to being correct, the lexicographers hand-corrected them to be good entries. In the next two sections we look at
how much correction was needed, for first the Japanese, and then the English halves.
All results are given looking only at those entries judged as possible: 45 intransitive
and 138 transitive entries.

4.4.7 Japanese SideThe Japanese results are summarized in Table 4.3 (note: a single entry may have morethan one part corrected). 82% of the entries needed no correction. In particular, theVi entries were good 93% of the time. The most common change was to tweak thesemantic restrictions.
The changes in SRs of the transitive verbs are shown in Table 4.4. Most of thetime the change was in the A argument (which was given the restriction h3:agenti
by default). The majority of the corrections were making the A’s SR more restrictive:changing the semantic class to its descendant. The corrections to the O’s SR werevarious. They were mainly made to reflect the fact that not all uses of a verb canalternate, the O’s SR is not always the same as the S’s, as was showed in Section 4.3.
79

Table 4.3: Japanese Evaluation (Fine Tuning)
Part Corrected
SRs
Case-role and Case-marker
Case-marker (only)
Japanese O.K.

Vi Created Vt Created
No.
% No.
%

Total
No.

2
4.4 19 20.4
2
4.4
1
1.1
0
0
2
2.2
42 93.3 71 76.3
45 entries 93 entries

21 15.23
2.2
2
1.4
113 81.9
138 entries%

Table 4.4: Analysis of Corrected SR Vt
How to Correct SR

Case-role (No.)
A

O

S

XI

Add
2
Delete0
Subsumed or Lower Level SR
9Lower Level SR (not subsumed) 2
Same Level SR
0Higher Level SR
0
Total
13

4
1
0
1
1
0
7

0
0
1
0
0
0
1

0
0
0
0
0
1
1

Table 4.5: English Evaluation (Fine Tuning)
Part Corrected
English Verb
SubcatOther element
English O.K.
Both Japanese and English O.K.

Vi Created Vt Created
No.
% No.
%

Total
No.

14 31.1 34 36.6
14 31.1 34 36.6
16 35.6 42 45.2
29 64.4 49 52.7
29 64.4 48 51.6
45 entries 93 entries

48 34.8
48 34.8
58 42.078 56.5
77 55.8
138 entries80%4.4.8 English SideAs we predicted in Section 4.2, good English translations with the same main verb
could only be made around 56% of the time. Although many entries had to be corrected, the lexicographers found it to be faster than creating the new entries fromscratch.
The results are given in Table 4.5 (note: a single entry may have more than one
part corrected). We managed to make almost exactly as many good English entries as
we predicted. In general the intransitive entries are better than the transitive ones. Thisis because we was adding information to make the transitives, and deleting it to makethe intransitives.

4.5 Discussion
The above results show that alternations can be used to create rich monolingual entries,
and to some extent bilingual entries. In this section we discuss some of the reasons forerrors, and suggest ways to improve the method.

4.5.1 Rejecting Impossible Candidates
To make the construction fully automatic, a test for whether a Japanese entry is possible
or not is required.
One possibility would be to add a corpus based filter: if no entries can be foundthat fit the entry, then it should be rejected. The problem with this approach is thatmany of the entries we created were for infrequent verbs. The average frequency in 16
years of Japanese newspaper text was only 173, and 22 verbs never appeared, although
all were familiar to native speakers.
Another, more hopeful, possibility is to learn a classifier based on features in theentries themselves. The agentitivity of the SR of the created intransitive entries (themost problematic group, see Table 4.2), seems a good cue, only 5.6% of the SRs of theimpossible entries were subsumed by h3:agenti, compared to 14.9% of the possible
entries. A classifier could also be trained on the known alternation pairs, although they
provide only positive evidence.
814.5.2 Improving the English Translations
The numbers of the different types of translations are compared for the reference data
(Section 4.2), the entries created by our method (Section 4.4) and the entries correctedby expert lexicographers (Section 4.4.6) in Table 4.6. The first three rows show entries
with the same English main verb.
Table 4.6: A Comparison of Reference Data with Created Alternations

English Structure
Vi
Vt
S Vi

Vi
Vt
Reference Created Corrected Created Corrected
No. (%) No. (%) No. (%) No. (%) No. (%)

A Vt O138 30.0
S be Vt-ed A Vt O
91 19.8
S Vi/be Adj A Vc O Vi/Adj 30 6.5
Different Head
10 10.8

16 35.6
27 60.0
00
2 4.4

5 12.834 87.2000031 33.3
8 8.6
44 47.3
10 10.8

17
8
32
18

Total

45 10039 10093 10075 100259 56.3

22.7
10.7
42.7
24.0

We focus on three discrepancies:
1. In the class “S Vi ⇔ A Vt O”, There are fewer ”Corrected” entries than ”Created”
for both Vi and Vt.
2. In the class “S Vi/be Adj ⇔ A Vc O Vi/Adj”, there are no entries for Vi but manyfor Vt.
3. The total No. of “Corrected” is less than “Created”. For 24 entries (17.4 %) the
lexicographers chose a different English translation.
The first discrepancy is caused by our implementation over estimating the number
of English verbs that undergo the S = O alternation. We used the very simple approximation that any English verb that had both transitive and intransitive entries in our
lexicon could undergo the alternation. This overestimates for two reasons (i) the verbs
may have different meanings, and thus not be alternations at all; (ii) the verbs may
undergo other alternations, such as A = S. Looking at the corrected data, in Vi, 7 entries are corrected to ”S be Vt-ed” from S Vi. In Vt, 3 entries are corrected to ”A Vc
82

O Vi/Adj” and 6 to different English verbs and the other 6 are corrected to intransitive

verbs in different constructions, such as (20) (created from
[X:h1000:abstracti-de] kagayaku “S shine with X”).
(20)%h℄( S:h555:facei-gakD  h$2

N1:h3:agenti
N2:h555:facei
N12:h1000:abstracti /
N1
ga N2
o
ni/de kagayakasu
N12
N1
NOM N2
ACC N12by shine“N1’s N2 shine with N12”

The second discrepency is in the frequency of the control verb construction. In
Vi, no original transitive entry used control verbs. In general, when the lexicographerscreate an entry, they prefer a simple entry to a synthetic one. Looking at the linguists’reference data, about 6.5% of the examples used control verbs. In the constructed data,
47.3 % (44 entries) use the control verb make, more than any other category. Of those
44 entries, 17 entries are corrected by the lexicographers. 8 entries are corrected to intransitive verbs; 5 entries are corrected to different English head and 2 were corrected
to different control verbs (let or have), the remain 2 were corrected to A Vt O. For example, when the original intransitive entry is N1 be exhausted, exhausted is defined as
adjective in the existing dictionary. So we create a new entry N1 make N2 exhaustedadj .
However, because exhaust is a transitive verb, it was corrected to N1 exhaust N2. The
algorithm needs to optionally convert adjectives to verbs in cases where there is overlap
between the adjective and past particle.
Finally, we consider those Japanese alternations where the transitive and intransitive alternatives need translations with diiferent English main verbs. A good example
nakunaru “S pass away” and Vt
nakusu “A lose O”.6of this is Vi
These are impossible to generate using our method. Even with reliable English syntactic data, it would be hard to rule out pass away as a possible transitive verb or lose
as an intransitive. They can only be ruled out by using data linking the subcat withthe meaning, and this would need to be linked to the Japanese verbs’ meanings. Thismay become possible with larger linked multi-lingual dictionaries, such as those underconstruction in the Papillon project7 , but is not now within our reach.
In summary, we could improve the construction of the English translations by using
richer English information, especially about alternations.

(Cd

(2

6 My friend passed away ⇔ I lost my friend.
7 http://www.papillon-dictionary.org/

834.6 Future Work
This research can be extended in four ways. The first is to work on automaticallyrejecting impossible Japanese entries. The second is to use richer English information
about alternations to improve the quality of the English entries. Both of these will will
improve the quality of the created entries.
The third is to apply the method to other alternations, using either linguists’ data or
automatically acquired alternations (Oishi and Matsumoto, 1997; Bond et al., 2002).
The last is to carry out a task based evaluation, using the extended dictionary in amachine translation system.
In addition, new entries are being added to the seed lexicon from a variety ofsources. When these new entries are one half of a known alternation, we apply thismethod to create the other half. Even with just this one alternation, we have already
added a hundred new entries in this manner (although not all were correct).
We hope that our extended lexicon will be useful not only for NLP applications,but for research into the nature of alternations themselves.

4.7 Conclusion
We presented a method that uses alternation data to add new entries to an existinglexicon. The new entries have detailed information about argument structure and selectional restrictions. If the existing lexicon has only one half of the alternation, thenour method constructs new Japanese entries with 69% of the verbs having one or morecorrect entries. We also showed that it is possible to simultaneously add entries to asecond language with a reduced accuracy of 56% if your exisiting lexicon has suchinformation. In this section we focused on one class of alternations, but it is applicable
to any alternation.

84

Chapter 5
Exploiting Semantic Information for
HPSG Parse Selection
In this chapter, we investigate the use of semantic information in parse selection.We
present that sense-based semantic features combined with ontological information are
effective for parse selection. Training and testing on the definition and example subsetof the Hinoki corpus (See Section 2.4), a combined model give a improvement in parseselection accuracy over a model using only syntactic features1 .

5.1 Introduction
Recently, significant improvements have been made in combining symbolic and statistical approaches to various natural language processing tasks. In parsing, for example,
symbolic grammars are combined with stochastic models (Oepen et al., 2004; Maloufand van Noord, 2004). Much of the gain in statistical parsing using lexicalized models comes from the use of a small set of function words (Klein and Manning, 2003).
Features based on general relations provide little improvement, presumably because
the data is too sparse: in the Penn treebank standardly used to train and test statisticalparsers stocks and skyrocket never appear together. However, the superordinate concepts capital (⊃ stocks) and move upward (⊃ sky rocket) frequently appear together,which suggests that using word senses and their hypernyms as features may be useful
1 We reported about this experiment in Fujita et al. (2007).

85

However, to date, there have been few combinations of sense information together
with symbolic grammars and statistical models. We hypothesize that one of the reasonsfor the lack of success is that there has been no resource annotated with both syntactic
and semantic information. In this chapter, we use a Hinoki corpus (See Section 2.4) ,with both syntactic information (HPSG parses) and semantic information (sense tags
from a Lexeed lexicon (See Section 2.3)). We use this to train parse selection models
using both syntactic and semantic features. A model trained using syntactic features
combined with semantic information outperforms a model using purely syntactic information by a wide margin (69.4% sentence parse accuracy vs. 63.8% on definition
sentences).

5.2 Parse Selection
Combining the broad-coverage JACY grammar and the Hinoki corpus, we build a parseselection model on top of the symbolic grammar. Given a set of candidate analyses(for some Japanese string) according to JACY, the goal is to rank parse trees by theirprobability: training a stochastic parse selection model on the available treebank, weestimate statistics of various features of candidate analyses from the treebank. The
definition and selection of features, thus, is a central parameter in the design of aneffective parse selection model.

5.2.1 Syntactic Features
The first model that we trained uses syntactic features defined over HPSG derivation
trees as summarized in Table 5.1. For the closely related purpose of parse selection
over the English Redwoods treebank, Toutanova et al. (2005) train a discriminative
log-linear model, using features defined over derivation trees with non-terminals representing the construction types and lexical types of the HPSG grammar. The basic
feature set of our parse selection model for Japanese is defined in the same way (corresponding to the PCFG-S model of Toutanova et al. (2005)): each feature capturing a
sub-tree from the derivation limited to depth one. Table 5.1 shows example features
extracted from our running example (Figure 2.10 in Section 2.4) in our MaxEnt models. In Table 5.1, the feature template #1 corresponds to local derivation sub-trees. We
86

Table 5.1: Example structural features (SYN-1 and SYN-GP) extracted from the derivation tree in Figure 2.10#

sample features

1
1
1
2
2
2
2
3
3
3
4
4
4

h0 rel-cl-sbj-gap hd-complement noun-lei
h1 frag-np rel-cl-sbj-gap hd-complement noun-lei
h2 △ frag-np rel-cl-sbj-gap hd-complement noun-lei
h0 rel-cl-sbj-gap hd-complementi
h0 rel-cl-sbj-gap noun-lei
h1 frag-np rel-cl-sbj-gap hd-complementi
h1 frag-np rel-cl-sbj-gap noun-lei
h1 conj-le yai
h2 noun-le conj-le yai
h3 ⊢ noun-le conj-le yai
h1 conj-lei
h2 noun-le conj-lei
h3 ⊢ noun-le conj-lei

The first column numbers the feature template corresponding to each example; in the examples, the first
integer value is a parameter to feature templates, i.e. the depth of grandparenting (types #1 and#2) or
n-gram size (types #3 and #4). The special symbols △ and ⊢ denote the root of the tree and left peripheryof the yield, respectively.

will refer to the parse selection model using only local structural features as SYN-1.

Dominance Features
To reduce the effects of data sparseness, feature type #2 in Table 5.1 provides a backoff to derivation sub-trees, where the sequence of daughters is reduced to just the headdaughter. Conversely, to facilitate sampling of larger contexts than just sub-trees ofdepth one, feature template #1 allows optional grandparenting, including the upwards
chain of dominating nodes in some features. In our experiments, we found that grandparenting of up to three dominating nodes gave the best balance of enlarged context vs.
data sparseness. Enriching our basic model SYN-1 with these features we will hence87

forth call SYN-GP.
N-Gram Features
In addition to these dominance-oriented features taken from the derivation trees of eachparse tree, our models also include more surface-oriented features, viz. n-grams of lexical types with or without lexicalization. Feature type #3 in Table 5.1 defines n-grams ofvariable size, where (in a loose analogy to part-of-speech tagging) sequences of lexical
types capture syntactic category assignments. Feature templates #3 and #4 only differ
with regard to lexicalization, as the former includes the surface token associated withthe rightmost element of each n-gram (loosely corresponding to the emission probabilities in an HMM tagger). We used a maximum n-gram size of two in the experiments
reported here, again due to its empirically determined best overall performance.

5.2.2 Semantic Features
In order to define semantic parse selection features, we use a reduction of the full semantic representation (MRS) into ‘variable-free’ elementary dependencies. The conversion centrally rests on a notion of one distinguished variable in each semantic relation. For most types of relations, the distinguished variable corresponds to the mainindex (ARG0 in the examples above), e.g. an event variable for verbal relations and areferential index for nominals. Assuming further that, by and large, there is a uniquerelation for each semantic variable for which it serves as the main index (thus assuming, for example, that adjectives and adverbs have event variables of their own, which
can be motivated in predicative usages at least), an MRS can be broken down into a
set of basic dependency tuples of the form shown in Figure 2.9 (Oepen and Lønning,2006).
All predicates are indexed to the position of the word or words that introduced
them in the input sentence (<start:end>). This allows us to link them to the sense
annotations in the corpus.
Basic Semantic Dependencies
The basic semantic model, SEM-Dep, consists of features based on a predicate andits arguments taken from the elementary dependencies. For example, consider the88

Table 5.2: Example semantic features (SEM-Dep) extracted from the dependency tree
in Figure 2.9.
#

sample features

20
20
21
21
21
21
22
23
23
...

h0 unten s ARG1 hito n 1 ARG2 ya p conji
h0 ya p conj LIDX densha n 1 RIDX jidousha n 1i
h1 unten s ARG1 hito n 1i
h1 unten s ARG2 jidousha n 1i
h1 ya p conj LIDX densha n 1i
h1 ya p conj RIDX jidousha n 1i
h2 unten s hito n 1 jidousha n 1i
h3 unten s hito n 1i
h3 unten s jidousha n 1i

dependencies for densha ya jidousha-wo unten suru hito “a person who drives a train
or car” given in Figure 2.9. The predicate unten “drive” has two arguments: ARG 1 hito
“person” and ARG 2 jidousha “car”.
From these, we produce several features (See Table 5.2). One has all arguments andtheir labels (#20). We also produce various back offs: #21 introduces only one argument at a time, #22 provides unlabeled relations, #23 provides one unlabeled relation
at a time and so on.
Each combination of a predicate and its related argument(s) becomes a feature.
These resemble the basic semantic features used by Toutanova et al. (2005). We further simplify these by collapsing some non-informative predicates, e.g. the unknown
predicate used in fragments.
Word Sense and Semantic Class Dependencies
We created two sets of features based only on the word senses. For SEM-WS we used
the sense annotation to replace each underspecified MRS predicate by a predicate indicating the word sense. This used the gold standard sense tags. For SEM-Class, we used
the sense annotation to replace each predicate by its Goi-Taikei semantic class.
89

Table 5.3: Example semantic class features (SEM-Class).
#

sample features

40
40
40
40
41
41
...

h0 unten s ARG1 C4 ARG2 C988i
h1 C2003 ARG1 C4 ARG2 C988i
h1 C2003 ARG1 C4 ARG2 C988i
h0 ya p conj LIDX C988 RIDX C988i
h2 unten s ARG1 C4i
h2 unten s ARG2 C988i

In addition, to capture more useful relationships, conjunctions were followed down
into the left and right daughters, and added as separate features. The semantic classesfor
1 densha “train” and1 jidousha “car” are both h988:land vehiclei,
while
1 unten “drive” is h2003:motioni and4 hito “person”is h4:humani. The
sample features of SEM-Class are shown in Table 5.3.
These features provide more specific information, in the case of the word sense,and semantic smoothing in the case of the semantic classes, as words are binned into
only 2,700 classes.

\
þU

 ¥

0

Superordinate Semantic Classes
We further smooth these features by replacing the semantic classes with their hypernyms at a given level (SEM-L). We investigated levels 2 to 5. Predicates are binned into
only 9 classes at level 2, 30 classes at level 3, 136 classes at level 4, and 392 classes atlevel 5.
For example, at level 3, the hypernym class for h988:land vehiclei is h706:
inanimatei, h2003:motioni is h1236:human activityi and h4:humani is unchanged. So we used h706:inanimatei and h1236:human activityi to make features inthe same way as Table 5.3.
An advantage of these underspecified semantic classes is that they are more robust to errors in word sense disambiguation — fine grained sense distinctions can beignored.
90

Valency Dictionary Compatability
The last kind of semantic information we use is valency information, taken from theJapanese side of the Goi-Taikei Japanese-English valency dictionary as extended by
Chapter 3. This valency dictionary has detailed information about the argument properties of verbs and adjectives, including subcategorization and selectional restrictions(For more details, see Section 2.2). A simplified entry of the Japanese side forunten-suru “drive” is shown in Figure 5.1.
Each entry has a predicate and several case-slots. Each case-slot has informationsuch as grammatical function, case-marker, case-role (N1, N2, . . . ) and semantic restrictions. The semantic restrictions are defined by the Goi-Taikei’s semantic classes.
On the Japanese side of Goi-Taikei’s valency dictionary, there are 10,146 types ofverbs giving 18,512 entries and 1,723 types of adjectives giving 2,618 entries.

þU2dPID : 300513 (PID is the verb’s Pattern ID.)
N1 h4:peopleiga
N2 h986:vehiclesiounten-suru “drive”

 þU2d%k

Figure 5.1:

þU2dunten-suru “N1 drive N2”.

The valency based features were constructed by first finding the most appropriate
pattern, and then recording how well it matched.
To find the most appropriate pattern, we extracted candidate dictionary entries
whose lemma is the same as the predicate in the sentence: for example we look up all
unten-suru “drive”. Then, for each candidate pattern, we mapped
entries for
its arguments to the target predicate’s arguments via case-markers. If the target predicate has no suitable argument, we mapped to comitative phrase. Finally, for eachcandidate patterns, we calculate a matching score2 and select the pattern which has thebest score.
Once we have the most appropriate pattern, we then construct features that record
how good the match is (Table 5.4). These include: the total score, with or without theverb’s Pattern ID (High/Med/Low/Zero: #31 0,1), the number of filled arguments (#31

þU2d

2 The scoring method follows Bond and Shirai (1997), and depends on the goodness of the matchesof the arguments.

91

Table 5.4: Example semantic features (SP)#sample features

31
31
31
31
31
32
32
33
33
...

h0 Highi
h1 300513 Highi
h2 2i
h3 R:Highi
h4 300513 R:Highi
h1 unten s Highi
h4 unten s R:Highi
h5 N1 C Highi
h7 Ci2), the fraction of filled arguments vs all arguments (High/Med/Low/Zero: #31 3,4),
the score for each argument of the pattern (#32 5) and the types of matches (#32 5,7).
These scores allow us to take advantage of information about word usage in an
exisiting dictionary.

5.3 Evaluation and Results
We trained and tested on a subset of the dictionary definition and example sentences inthe Hinoki corpus. This consists of those sentences with ambiguous parses which havebeen annotated so that the number of parses has been reduced (Table 5.5). That is, we
excluded unambiguous sentences (with a single parse), and those where the annotators
judged that no parse gave the correct semantics. This does not necessarily mean that
there is a single correct parse, we allow the annotator to claim that two or more parses
are equally appropriate.
Dictionary definition sentences are a different genre to other commonly used test
sets (e.g. newspaper text in the Penn Treebank or travel dialogues in Redwoods).
However, they are valid examples of naturally occurring texts and a native speaker
can read and understand them without special training. The main differences with
92

Table 5.5: Data of Sets for Evaluation
Corpus# Sents Length Parses/Sent
(Ave)
(Ave)

Definitions Train
Test
Examples Train
Test

30,3452,790
27,081
2,587

9.3
10.1
10.9
10.4

190.1
177.0
74.1
47.3

newspaper text is that the definition sentences are shorter, contain more fragments(especially NPs as single utterances) and fewer quoting and proper names. The maindifferences with travel dialogues is the lack of questions.

5.3.1 A Maximum Entropy Ranker
Log-linear models provide a very flexible framework that has been widely used for arange of tasks in NLP, including parse selection and reranking for machine translation.
We use a maximum entropy / minimum divergence (MEMD) modeler to train the parseselection model. Specifically, we use the open-source Toolkit for Advanced Discriminative Modeling (TADM:3 Malouf, 2002) for training, using its limited-memory
variable metric as the optimization method and determining best-performing convergence thresholds and prior sizes experimentally. A comparison of this learner withthe use of support vector machines over similar data found that the SVMs gave comparable results but were far slower (Baldridge and Osborne., 2007). Because we areinvestigating the effects of various different features, we chose the faster learner.

5.3.2 Results
The results for most of the models discussed in the previous section are shown in
Table 5.6. The accuracy is exact match for the entire sentence: a model gets a point
only if its top ranked analysis is the same as an analysis selected as correct in Hinoki.
This is a stricter metric than component based measures (e.g., labelled precision) which
3 http://tadm.sourceforge.net

93

Table 5.6: Parse Selection Results
Method

SYN-1
SYN-GPSYN-ALL
SYN baseline
SEM-Dep+SEM-WS+SEM-Class+SEM-L2+SEM-L3+SEM-L4+SEM-L5+SP+SEM-ALLSYN-SEMSEM baseline

Definitions
ExamplesAccuracy Features Accuracy Features(%) (×1000)(%) (×1000)
52.8
7
67.6
8
62.7
266
76.0
196
63.8
316
76.2
245
16.4 random
22.3 random
57.3
1,189
58.7
675
56.2
1,904
59.0
1,486
57.5
2,018
59.7
1,669
60.3
808
62.9
823
59.8
876
62.8
879
59.9
1,000
62.3
973
60.4
1,240
61.3
1,202
59.1
1,218
68.2
819
62.7
3,384
69.1
2,693
69.5
2,476
79.2
2,126
20.3

random22.8

randomaward partial credit for incorrect parses. For the syntactic models, the baseline (random
choice) is 16.4% for the definitions and 22.3% for the examples. Definition sentences
are harder to parse than the example sentences. This is mainly because they havemore relative clauses and coordinate NPs, both large sources of ambiguity. For thesemantic and combined models, multiple sentences can have different parses but thesame semantics. In this case all sentences with the correct semantics are scored as
good. This raises the baselines to 20.3 and 22.8% respectively.
Even the simplest models (SYN-1 and SEM-Dep) give a large improvement overthe baseline. Adding grandparenting to the syntactic model has a large improvement(SYN-GP), but adding lexical n-grams gave only a slight improvement over this (SYNALL).
94

70
60
Sent. Accuracy (%)

ld

ld

+bc

+bc

ld

+bc

ld

+bc

ld

ld

+bc

+bc

ld

ld

+bc

+bc

+bc

80

90 100

ld

ld

+bc

50
SYN-SEM
ld

40

SEM-ALL+SYN-ALL
bc

30
20 +
0
ldbc

10

20

30

40

50

60

70

% of training data (30,345 sentences)

Figure 5.2: Learning Curves (Definitions)

The effect of smoothing by superordinate semantic classes (SEM-Class), shows amodest improvement. The syntactic model already contains a back-off to lexical-types,
we hypothesize that the semantic classes behave in the same way. Surprisingly, as we
add more data, the very top level of the semantic class hierarchy performs almost as
well as the more detailed levels. The features using the valency dictionary (SP) also
provide a considerable improvement over the basic dependencies.
Combining all the semantic features (SEM-ALL) provides a clear improvement, suggesting that the information is heterogeneous. Finally, combing the syntactic and semantic features gives the best results by far (SYN-SEM: SYN-ALL + SEM-Dep + SEMClass + SEM-L2 + SP). The definitions sentences are harder syntactically, and thus get
more of a boost from the semantics. The semantics still improve performance for theexample sentences.
The semantic class based sense features used here are based on manual annotation,and thus show an upper bound on the effects of these features. This is not an absolute
upper bound on the use of sense information — it may be possible to improve further
through feature engineering. The learning curves (Fig 5.2) have not yet flattened out.
We can still improve by increasing the size of the training data.
95

5.4 Discussion
Bikel (2000) combined sense information and parse information using a subset of SemCor (with WordNet senses and Penn-II treebanks) to produce a combined model. Thismodel did not use semantic dependency relations, but only syntactic dependencies
augmented with heads, which suggests that the deeper structural semantics provided
by the HPSG parser is important. Xiong et al. (2005) achieved only a very minor improvement over a plain syntactic model, using features based on both the correlation
between predicates and their arguments, and between predicates and the hypernymsof their arguments (using HowNet). However, they do not investigate generalizing
to different levels than a word’s immediate hypernym. Resently, Agirre et al. (2008)shows that semantic classes help to obtain significant improvement in both parsing and
PP attachment tasks. They tested using English dataset: Penn Treebank, SemCor and
WordNet.
Pioneering work by Toutanova et al. (2005) and Baldridge and Osborne. (2007)
on parse selection for an English HPSG treebank used simpler semantic features without sense information, and got a far less dramatic improvement when they combinedsyntactic and semantic information.
The use of hand-crafted lexical resources such as the Goi-Taikei ontology is sometimes criticized on the grounds that such resources are hard to produce and scarce.
While it is true that valency lexicons and sense hierarchies are hard to produce, they
are of such value that they have already been created for all of the languages we knowof which have large treebanks. In fact, there are more languages with WordNets thanlarge treebanks.
In future work we intend to confirm that we can get improved results with raw sense
disambiguation results not just the gold standard annotations and test the results onother sections of the Hinoki corpus. To get the high-quality raw sense disambiguation
results, we propose a word sense disambiguation method in Chapter 6.

5.5 Conclusions
We have shown that sense-based semantic features combined with ontological information are effective for parse selection. Training and testing on the definition subset
96

of the Hinoki corpus, a combined model gave a 5.6% improvement in parse selection
accuracy over a model using only syntactic features (63.8% → 69.4%). Similar results(76.2% → 79.2%) were found with example sentences.

97

98

Chapter 6
Word Sense Disambiguation using
Disambiguated Superordinate
Semantic Classes
In this chapter, we propose a new method for word sense disambiguation (WSD) using
superordinate semantic classes. We separate WSD into two stages. In the first stage,
we determine superordinate semantic classes, then in the second stage we determine
fine-grained word senses using the results of the first stage. In the second stage, by using superordinate semantic classes, we show an improvement over the best publishedmethod of Japanese dictionary-based lexical-sample task of SENSEVAL-2 2. In addition, we show the effectiveness of superordinate semantic classes for unseen words
1.

6.1 Introduction
In this chapter, we propose a method for word sense disambiguation (WSD) using Superordinate Semantic Classes. Many words have multiple meanings, and they change
depending on the context. WSD has been shown to be useful in a variety of NLP applications including parse selection (Fujita et al., 2007) and machine translation (Chanet al., 2007).
1 We reported this in Fujita et al. (2008)

99

There is much previous research on WSD. In the SENSEVAL-2 Japanese lexicaltask, supervised systems using a large number of shallow features did the best (Murata
et al., 2003). More recently, unsupervised approaches such as extended Lesk havebeen shown to do well (Baldwin et al., 2008), although they are beaten by supervised
approaches using both semantic and syntactic features (Tanaka et al., 2007).
However, we would like to use the WSD results to improve the accuracy of ourparsing, so we cannot use the results of syntactic analysis to restrict the senses. So, in
this chapter, we propose a WSD method that does not use syntactic information.
Now, we consider the reason why supervised WSD is difficult. One importantreason is the difficulty of constructing enough training data for large sense inventories.
When there are several tens or hundreds of thousands of word senses, it is very difficult
to get enough training data for all the words. In addition, the number of classes makes
it hard to train standard machine learning tools.
Because of that, we separate WSD into 2 stages. In the first stage, we guess higherlevel (superordinate) semantic classes, such as person, place, thing, event. In the second stage, we deduce detailed word senses using the superordinate semantic classes asconstraints.
It is easier to disambiguate the superordinate semantic classes because the numberof higher level classes is much less than the number of word senses, therefore we canget enough accuracy using relatively less training data. In addition, many word senses
can already be decided just from the superordinate semantic class. This approach issimilar to Kohomban and Lee (2005), who used WordNet (Fellbaum, 1998) uniquebeginners (25 for nouns and 15 for verbs) which effectively divide WordNet senses
into coarser superordinate classes.
In the next section, we describe the resources which we use. Then, in Section 6.3,
we describe the superordinate semantic class disambiguation. In Section 6.4, we describe WSD using the superordinate semantic classes. In Section 6.5 and Section 6.6,
we discuss and describe future work, before concluding in Section 6.7.

6.2 Resources
We use the Hinoki corpus (See Section 2.4) to train both the superordinate semantic
class models (in Section 6.3) and the full WSD models (in Section 6.4).
100

À{ raitâ “lighter/writer/raita”

I NDEXPOSFAMILIARITY














S ENSE 1










noun












1
1
2
1
17










1





a device for lighting things, especially cigarettes.


 



1
2
1
17
 3
 


He lit the cigarette with his lighter




yougu
“device”
1h915:household appliancei (⊂ h706:inanimatei)

53815,0-0-1-0 (L ≃ R(2/3), L ⊃ R(1/3))

Lexical-type

6.2 [1–7]
D EFINITION







E XAMPLEH YPERNYMS EM . C LASSI WANAMInoun-lexWm H ¸D  8I, D m k W*d
8Z G X  H À{  8I, D m k W* 8 

XWhere Sem. Classes come from Goi-Taikei.; ⊂ shows subsumption (not necessarily direct).

Figure 6.1: Entry forÀ{1 raitâ “lighter” from Lexeed

The Hinoki corpus was annotated with both syntactic parses and semantic information (HPSG parses and sense tags from Lexeed, (Tanaka et al., 2006), see Section 2.4),
but in this section, we don’t use the syntactic information, as we wish to use the WSD
results in parse selection in future work.
We described resources which we use in Chapter 2. we also show a (simplified)example of an Lexeed entry in Figure 6.1.
All words in the 28,000 word fundamental vocabulary of Hinoki are tagged withword senses of Lexeed, which are in turn linked to the Goi-Taikei semantic classes. Anytabakowords outside of this vocabulary are untagged. For example, the word“cigarette” (of example sentence in Figure 6.1) is tagged as sense 2 in the example
sentence, with the meaning “cigarette” not “tobacco plant” and this has the semantic
class h862:cigarettei. Each word was sense annotated by five annotators. We use
the majority choice as correct sense in case of disagreements (Tanaka et al., 2006).

8I,

Table 6.1 shows the number of word senses per semantic class. That is, it showsthe effect of constraining words senses using the higher semantic classes. For example, of all the polysemic senses (48,180), 56.7% word senses will be completely
101

Table 6.1: Number of word senses per semantic class (at each level)
# WS
per
class
1
2
3
4
≥5
Total

ALL Semantic
Classes
Lvl 5 (392)
Lvl 4 (136)
Lvl 3 (30)
Lvl 2 (9)
#
%
#
%
#
%
#
%
#
%
32,167 66.8 27,316 56.7 20,775 43.1 16,944 35.2 10,582 22.0
11,606 24.1 14,078 29.2 15,852 32.9 17,106 35.5 18,236 37.8
2,769
5.7 3,897 8.1 5,244 10.9 6,084 12.6 7,344 15.2
900
1.9 1,264 2.6 2,080 4.3 2,628 5.5 3,680 7.6738
1.5 1,625 3.4 4,229 8.8 5,418 11.2 8,338 17.3
48,180100 48,180 100 48,180 100 48,180 100 48,180 100

We use only one class for each word sense even if it’s linked to multiple semantic classes.

disambiguated by the superordinate semantic class at level 5. In addition, even if they
can’t be completely disambiguated, the number of choices is reduced, for example,
29.2 % have only two choices.

6.3 Superordinate Semantic Class Disambiguation
In this section we describe the construction of the superordinate semantic class selection model. In order to investigate which is the best level of superordinate semantic
classes to use in Disambiguating word senses, we investigated levels 2 to 5 of GoiTaikei. The word senses are binned into only 9 classes at level 2, 30 classes at level 3,
136 classes at level 4, and 392 classes at level 5.

6.3.1 Mapping Word Sense to Superordinate Semantic Class
Beacuse the semantic classes are in a hierarchy, we can simply generalize them into
superordinate classes. For example, in the case of the example sentence of
1 raitâ “lighter” (Figure 6.1), the semantic classes for2 tabako “cigarette”
is h862:cigarettei, while 1 hi “fire” is h2312:burning/combustioni and17 tsukeru “light” is h2004:operationi. At level 3, the superordinate class for



d

8I ,

m

102

À{
W*

(21)

8I,2

D m1

k W*17

8

cigarette
DAT fire
ACC light
TENSE
Sem. h862:cigarettei h2312:burning/h2004:operationi
Class
combustioni
Lvl 5 h893:equipment/ h2306:materialh1920:labori
tooli
phenomenoni
Lvl 4 h760:artifacti h2305:non-living h1560:act/conducti phenomenoni
Lvl 3 h706:inanimatei h2304:natural
h1236:human act.i phenomenoni
Lvl 2 h533:objectsi
h1235:eventsi
h1235:eventsi
Where phen. is abbreviation of phenomenon, and act. is activity.

h862:cigarettei is h706:inanimatei, h2312:burning/combustioni is h2304:natural phenomenai and h2004:operationi is h1236:human activityi. So we replace word senses into superordinate semantic classes as shown in (21)2 , which shows
the actual semantic class for each content word and the superordinate terms at levels 2
to 5. The more specific sense (the hyponym), is shown lower here, thus h2004:operationi ⊂ h1920:labori ⊂ h1560:act/conducti ⊂ h1236:human activityi ⊂ h1235:
eventsi.

6.3.2 Problems in Mappings
Because Lexeed and Goi-Taikei were developed separately, there are some inconsistencies in the hierarchies. Generally, Lexeed is more fine-grained, but occasionally asingle Lexeed sense will be linked to multiple semantic classes in Goi-Taikei. In thiscase we use the first listed class (which should is the most frequent class (Ikehara et al.,1997)).
For example, in Lexeed, the simplified definition of word ushi “beef/cow” is “Akind of mammal. It’s milk and meat are edible.”. The Lexeed sense inventory does
not distinguish between the animal and its meat or milk. However, Goi-Taikei links
ushi “beef/cow” to both h537:beasti and h843:meat and eggsi. Thus, for example,¤¤

2 We use the following abbreviations: ACC: accsative postposition; DAT : dative postposition; LOC:

locative postposition.

103

¤

in (22), the word ushi “beef/cow” should be tagged with h537:beasti (at level 3,h534:animatei). In contrast, in (23), it should be tagged with h843:meat and eggsi(at level 3, h706:inanimatei). But in this experiment, both are tagged with the firstclass, that is h537:beasti (at level 3, h534:animatei)3 . Note that both h537:beastiand h843:meat and eggsi are merged into h533:objectsi at level 2.
(22)

e

 ¤ k ßfarm family LOC cow ACC keep
“A farm family keeps cows.”(23)¨  ¤ k 6supermarket LOC beef ACC buy“I buy beef in supermarket.”This is a problem with the granularity of Lexeed, which conflates the animal andmeat senses of ushi “beef/cow” in a single entry.

¤

Table 6.2 shows the number of semantic classes per word sense at each level. Evenat level 5, more than 70% Lexeed word senses have only one superordinate semantic
class.
Table 6.2: Number of Semantic Classes per word senseclasses/senseLvl 2
No (%)

1
2
3
4
5
≥6

39,654
6,101
323
19
4
0

Total

46,101

86.0
13.2
0.7
0.0
0.0
0.0

Lvl 3
No (%)
36,928
8,409
683
55
18
8
46,101

80.1
18.2
1.5
0.1
0.0
0.0

Lvl 4
No (%)
35,075
9,858
1,018
100
21
29
46,101

76.1
21.4
2.2
0.2
0.0
0.1

Lvl 5
No (%)
32,496
11791
1,517
199
41
57
46,101

70.5
25.6
3.3
0.4
0.1
0.1

Class
No (%)
30,558
13,102
1,955
345
62
79
46,101

3 To solve this problem, we are annotating Hinoki by correct Goi-Taikei’s semantic classes.

104

66.3
28.4
4.2
0.7
0.1
0.2

6.3.3 Data used
We trained and tested on the dictionary definition (Def.) and example (Ex.) sentences
and Kyoto Corpus (KC) in the Hinoki corpus. In this chapter, we assume that morphological analysis has been done, and we use the results of morphological analysis as
inputs.
We divided the data into training and test data. Table 6.3 shows the size of the data
sets for training and test. Target words are those open class words tagged with Lexeed
senses.
Note that several word and word senses appeared in the test data which did notappear in the training data (in the case of Def. 19 words and 389 senses are missing in
the training data, for Ex. 1,038 words and 1614 senses, and for KC 137 words and 267
senses). Generalizing to superordinate semantic classes alleviates this data sparsenessproblem.
Table 6.3: Data Sets for Superordinate Classes (All Words)# Sents # Target Words # All Words

CorpusSet

Def.

Train 67,202
4,942
Test
Train 106,528
8,942
Test
Train 35,440
Test
2,000

Ex.
KC

175,70915,436
133,616
11,043
211,567
12,123

613,216
54,276
432,51441,019947,298
53,7036.3.4 Method
Machine Learning Method We take a sequence labeling approach to make superordinate semantic class disambiguation models, because our goal is to get a wide coverage and robust word sense tagger, not only for a few target words. Ciaramita andAltun (2006) applied Perceptron-trained Hidden Markov Model (HMM) to estimate
supersenses of WordNet. But we use Conditional Random Fields: CRF (Suzuki et al.,105

Lattice (paths of possible superordinate semantic classes at Level 3)hanim.i
hanim.i
hanim.i
hanim.i
hanim.i
B
O
S

hin-

h-i

anim.ih-i

hnaturalphen.ihhumanhanim.i
h-i

act.i

hplacei

hplacei

hplacei

hplacei

hplacei

hplacei

h...i

h...i

h...i

h...i

h...i

h...i

E
O
S

Where phen. is abbreviation of phenomenon, act. is activity, and anim. is animate.
The bold line shows the correct path.
Input
w
b
cigarette
DAT
fire
ACC
light
TENCE
p1
noun
particlenounparticleverb
aux verb
p2 noun-com
particle-cm
noun-com
particle-cm
verb-independent aux verb-*
p3 noun-com-* particle-cm-com noun-com-* particle-cm-com verb-independent-* aux verb-*

8I,
8I,

D
D

m
m

k
k

W*
W*d

8
8

We use the following abbreviations: cm: casemarker; aux: auxiliary; com: common(general).

Figure 6.2: Simplified Example of Input Information and (Ideal) Lattice of Possible
Superordinate Semantic Classes (Level 3)2006).We select CRF because it allows relaxation of the strong independence assumptions made by HMMs and has performed well for similar sequential labeling problems
such as part-of-speech (POS) tagging (Lafferty et al., 2001; Kudo et al., 2004) and
named entity recognition (Suzuki et al., 2006).
We can’t apply CRF directly to the full WSD problem because the number ofclasses of senses is too large. But, by restricting ourselves to superordinate semantic classes the number of classes is reduced enough to enable us to train.

106

Features Now, we describe the features used by CRF. We use uni-gram, bi-gram andcombinations of morphological information: that is the word itself (w), base form (b),main category of POS (p1), sub-category of POS (p2) and sub-sub-category of POS(p3). We make features from two words on either side of the target word. That is, inthe case of the target is ith word/morpheme, we use the information from the i − 2th toi + 2th morphemes.

Hard Dictionary Constraints Figure 6.2 shows a simplified example of input andused information and lattice of possible superordinate semantic classes at Level 3.
We select the best path (which has the best score) of superordinate semantic classes.
The learner considers all superordinate semantic classes, and thus may predict a classthat is not used in the dictionary for this word. For example, at level 3, the only
tabako “tobacco plant/cigarette” are h534:animatei andpossible classes for
h706:inanimatei according to the entry in Lexeed. But the system may guess a different class, such as h388:placei. To fix such impossible errors, we relabel any words
marked with classes not found in the lexicon with the most frequent possible semantic
class. This happens between 1–7% of the time, depending on the level.

8I,

6.3.5 Results and Discussion
Table 6.4 shows the results of superordinate semantic class disambiguation using CRF.
The system actually chooses the semantic classes for all of the words including monosemous words.But in Table 6.4, we show the results for polysemous words: that is thetarget words shown in Table 6.3.
The baseline (BL) method selects the most frequent semantic class from all possible semantic classes for all senses of the target word. As we can see in Table 6.4, CRF
gives much better results than the baseline, especially at deep levels. But CRF needs
much time and memory. So, we get some scores (underlined) without p2 (the POS
subcategry), on average, the scores without p2 drop down 0.1-0.2 % from the scoreswith p2.
In Table 6.4, the results labeled with Hard shows the results with the hard dictionary constraints applied (see Section 6.3.4). The Hard results are better than raw CRF
for all combinations, and we will use these results in the next section.
107

Table 6.4: Results of Superordinate Semantic Class Disambiguation

CorpusDefinitions

ExamplesKyoto Corpus

Level noun verb adj misc

total noun verb adj misc

total noun verb adj misc

total

BL 2
3
4
5

89.7 96.3 81.7 98.0
84.2 85.1 68.5 96.1
77.7 83.7 71.3 94.1
70.9 70.6 60.0 60.8

91.3 84.5 93.4 87.2 100.0
83.6 78.5 84.4 74.0 95.2
79.3 74.9 80.9 72.2 95.2
70.1 68.7 67.0 57.5 52.4

87.4 89.9 94.4 70.5 80.9
80.2 84.1 83.4 55.8 79.4
76.7 80.9 79.9 63.9 77.9
67.7

90.3
83.3
80.3

CRF 2
3
4
5

96.0 97.4 88.2 88.2
93.9 90.5 80.1 88.2
92.5 89.2 78.7 88.2
88.7 82.5 74.3 84.3

96.0 85.2 95.8 89.7 85.7
92.0 81.9 89.1 77.6 81.0
90.6 79.8 87.2 76.4 81.0
85.9 76.6 80.6 69.8 81.0

88.7 93.1 94.3 87.7 58.8
84.0 91.3 86.2 81.8 58.8
82.0 89.7 84.6 76.5 57.4
77.6

93.0
89.8
88.2

Hard 2
3
4
5

96.3 97.5 89.0 96.1
94.4 90.9 81.1 94.1
93.0 89.8 79.7 96.1
89.5 83.5 75.2 88.2

96.3 90.2 95.8 89.9 100.0
92.5 87.3 89.3 78.1 95.2
91.2 85.4 87.4 76.9 95.2
86.7 82.9 80.9 70.3 95.2

92.0 96.6 96.0 89.1 80.9
87.6 95.1 88.7 83.5 79.4
85.7 93.6 87.3 78.2 79.4
81.9

96.2
93.4
91.9

Targets 9,575 4,895 915 51 15,436 7,189 3,426 407 21 11,043 9,303 2467 285
Where underlined figures were obtained with a simplified model
not using p2 (sub category of POS) as a feature.

108

68 12,123

Note that this may not be the desired behaviour for a completely open system:
senses may be missing in Lexeed, and allowing senses not in the lexicon could be beneficial. However, in this experiment, words can only be tagged with exisiting senses,
so we thus restrict them.

6.4 Word Sense Disambiguation (WSD)
In this section, to investigate the effectiveness of superordinate semantic classes for
WSD, we show 2 types of data. First, we describe the sense level WSD experimentusing the superordinate semantic classes which were extracted in Section 6.3.
Secondly, we show the effects on unseen words: words which appeared in the test
data which did not appear in the training data (Section 6.4.2).

6.4.1 Comparison with SENSEVAL-2 Japanese Task
First, we show the effectiveness of superordinate semantic classes on full WSD.
The best published result for the Japanese dictionary-based lexical-sample task of
SENSEVAL-2 is given by Murata et al. (2003). We therefore reimplemented their system for comparison. We call this reimplemented system, CRL′. Murata et al. (2003)
used SVM (Chang and Lin, 2001) as a learner with the following features (See Murata
et al. (2003) for more details.): uni/bi/tri-gram characters which precede and follow
the target word; Morphological features extracted from the results of morphologicalanalysis; syntactic features from a shallow dependency parser; coocurrence features
foremd from all morphemes in the same sentence; and Universal Decimal Classification (UDC) codes.
However, our implementation of CRL′ differs from Murata et al. (2003) in twoplaces: we do not use the syntactic features or the UDC codes. The reason that we
didn’t use syntactic features is that we believe that the results of WSD are useful forsyntactic parsing, so we don’t use syntactic parsing as pre-processing. We didn’t use
UDC features because the UDC codes are not tagged in the Hinoki Corpus. A further
difference is that , they used JUMAN/RWC for morphological analysis, but we used
ChaSen.
Our system, NEW, also uses SVM, and adds the superordinate semantic classes
109

(which were extracted in Section 6.3) as features to CRL′. These are added on theword itself, and the two words on either side. We experiment with superordinate
semantic classes generalized to different upper levels.
For example, in Figure 6.2, if we guess h1236:human activityi as the superortsuke “light” at level 3, we use
dinate semantic class corresponding to 5th word
both h1236:human activityi and h1235:eventsi as features.

ä*

Data for WSD
For the fine grained word sense disambiguation experiment, we use the same targetwords as SENSEVAL-2, in order to give a more meaningful comparison. There are
100 target words: 50 nouns and 50 verbs. The test documents were the same as in
SENSEVAL-2, with all text coming from newspaper articles (these are not part of
the training data). Table 6.5 shows the amount of training and test data used in thisexperiment.
Results and Discussion
Table 6.6 shows the results of the full WSD. The baseline (BL) method selects theword sense occurring most frequently in the training corpus. The higher baseline system (BL2) uses the most frequent sense restricted by the disambiguated superordinate
semantic classes.
NEW also uses the disambiguated superordinate semantic classes. All results are

significantly better than the baseline (BL). And most results of NEW are better than
CRL′ , even at upper levels.
In addition, Table 6.6 shows that even if we just use the most frequent sense restricted by the disambiguated superordinate semantic classes (BL2), we can get highaccuracy. In general, the more specific the superordinate classes, the higher the accuracy, even though the accuracy for disambiguating the more specific classes is lower.
The improvement is smallest for the Kyoto Corpus data. We hypothesize that this isbecause it has more unknown senses — none of the words not in Lexeed’s fundamental
vocabulary are tagged. In particular, proper nouns are not tagged. This means 25% of
the words (mainly noun phrases) have no sense information. In contrast, all the words
in the Example and Definition sub-corpora are in the fundamental vocabulary. We are110Table 6.5: Data Sets for WSD (Senseval 100 words)
noun

Corpus Set
Def.
Ex.
KC

verb

Trains 6512 11409
745 1151
Test
Trains 4448 7888
Test
317
826
Trains 11140 10744
Test
763
610

Table 6.6: Results of WSD (by SVM)
Definitions
CorpusLevel noun verb ave
BL
CRL′

BL2

2
3
4
5
NEW 2
3
4
5

74.5
81.1
76.8
80.8
80.9
83.4
81.3
81.5
81.6
81.7

56.8
65.3
59.9
60.6
61.6
67.4
65.6
66.1
66.3
67.2

63.8
71.5
66.5
68.5
69.2
73.7
71.8
72.2
72.3
72.9

Examplesnoun verb ave

Kyoto Corpus
noun verb ave

63.7
79.5
66.9
69.1
71.0
76.3
79.5
79.5
79.5
80.1

69.2
80.9
69.9
75.0
76.7

62.1
67.0
63.4
65.4
68.0

81.3
81.5
81.3

67.0 74.9
67.0 75.1
67.0 74.9

111

56.2
68.5
58.8
60.5
61.3
65.2
68.3
68.5
68.8
69.2

58.3
71.6
61.0
62.8
64.0
68.3
71.4
71.6
71.7
72.3

66.1
74.7
67.0
70.7
72.8

Table 6.7: Accuracy for words which didn’t appear in training data (Zero Frequency)
Definitions
ExamplesKyoto Corpus
CorpusLevel noun verb adj misc total noun verb adj misc total noun verb adj misc total
first sense 27.8 0.0 0.0NEW′ 2

3
4
5

55.6
61.1
55.6
50.0

# Targets

18

0.0 26.3 27.4 0.0 0.0

0.0 0.0 0.0 52.6
0.0 0.0 0.0 57.9
0.0 0.0 0.0 52.6
0.0 0.0 100.0 52.6
0

0

1

46.5
48.8
47.8
47.3

19 1,035

0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.030

0.0 27.4 36.5 0.0 0.0 23.1 29.9
0.0
0.0
0.0
0.0

46.3 43.3 20.0 40.0 30.8 39.4
48.7 53.8 33.3 20.0 46.2 49.6
47.7 40.4 40.0 60.0 53.8 42.3
47.2

0 1,038 10415

5

13 137currently the remaining words in the kyoto Corpus with Goi-Taikei semantic classes togive us the data to test this hypothesis.

6.4.2 Effect on Unseen Words
Because of the huge numbers of words and senses, it is very difficult to get enoughtraining data. Sometimes, we can get no training data at all for some words. In
such case, most supervised WSD methods (including CRL′) doesn’t work. But in our
method, at least, we can guess superordinate semantic classes for words which didn’t
appear in training data.
Table 6.7 shows the accuracy for such words (frequency is 0). In this case, we
compare the first sense (in Lexeed) baseline with the first sense restricted by the disambiguated superordinate semantic classes (NEW′ ). The accuracy of NEW′ is muchbetter than first sense baseline. Disambiguating superordinate semantic classes gives amuch more robust WSD system. It’s interesting to note that the superordinate semantic
classes at Level 3 give the best results overall, with an accuracy of 49%, compared tothe baseline of 27%.

6.5 Discussion
We showed that disambiguating superordinate semantic classes is an effective way of
WSD, even though we use superordinate classes from a different resource. This is112

important, as the sense inventories used in a task are not always in a full hierarchy(e.g., the Japanese SENSEVAL-2 task gave word senses from a dictionary with noassociated hierarchy). We expect we could get even better results using a hierarchy
built around the Lexeed word senses.
Further, we have shown that a quite large superordinate class inventory (level 5
with 393 results) gave the best results on several test sets. This suggests that work on
English using the unique beginners could possibly be improved by specializing evenfurther in the initial step.
It could be that the Lexeed semantic classes are too fine-grained for reliable sense
disambiguation. Navigli (2006) shows that this is true for the English WordNet—clustering senses allows for more reliable manual and automatic annotation. Bondet al. (2004) argue that, in comparison to Goi-Taikei, the finer granularity of Lexeed isnecessary for question answering, but it may still be the case that not all of the sense
distinctions are meaningful. Fujita et al. (2007) use gold-standard sense information to
improve parse selection, and found that the superordinate senses at level two were themost effective to reduce data sparseness for parse selection.

6.6 Future WorkIn future work we intend to confirm that we can get improved results in other languages
such as English using various levels of superordinate senses in WordNet (Agirre et al.,2008).
Then we intend to make a superordinate semantic class tagger using CRF like
MeCab (Kudo et al., 2004). That is, in this chapter, for the experiment, we used the
packaged CRF based machine learner, but if we save the possible pairs of entries andsuperordinate semantic classes into a dictionary, we will not have to fix impossible errors (That is we can get Hard data from the beginning). We hope that this will improve
the accracy even further. Alternatively, for morphological analysis, we may get partof-speech tag and sense tag together. In addition, we would like to further experimentwith limiting the number of states, more features and guessing superordinate semantic
classses at even deeper levels.
Finally, to get more training data for superordinate semantic class disambiguation,
we intend to use untagged corpus, by applying the method proposed by Tsuboi et al.
113

(2008).

6.7 Conclusion
In this chapter, we proposed the method for word sense disambiguation (WSD) using
superordinate semantic classes. At the first stage, we guess superordinate semantic
classes, then at the second stage we guess word senses using the results of 1st stage.
At the first stage, we applied CRF to superordinate semantic class disambiguation.
As a result, it gave us very high accuracy. At the second stage, we got higher accuracy
for WSD than published best method of Japanese dictionary-based lexical-sample taskof SENSEVAL-2. In addition, we showed the effectiveness of superordinate semantic
classes for unseen words.
In conclusion, our proposed WSD method using superordinate semantic classes isvery effective.

114

Chapter 7
Conclusion
7.1 Summary
In this thesis, we introduced some resources: Goi-Taikei, its bilingual valency patterndictionary, Hinoki, and Lexeed, which have rich information and then are related toeach other. First, we construct these resources by hand as shown in Chapter 2. Then,
we proposed a method to extend them effectively, and proved the usefulness throughseveral task-based evaluations.
In Chapter 3, we presented a method of extending the coverage of the bilingual
valency dictionary, by assigning valency information and selectional restrictions toentries in a bilingual dictionary. The method exploits existing bilingual valency dictionaries and is based on two basic assumptions: words with similar meaning have
similar subcategorization frames and selectional restrictions; and words with the sametranslations have similar meanings. A prototype system allowed 6,327 new patterns
to be built, using only simple human judgement (pre-filter). Of those more than 51%
were usable as is, and more than 36% were usable with minor revisions, giving 87.7%
potentially useful patterns. The cost, including human revisions, is less than 6 minutes
per pattern. Furthermore, even before applying human revisions, adding the createdpatterns to a Japanese-to-English machine translation system improved the translationfor 32% of sentences using these verbs, and degraded it for only 16%; a substantial
improvement in quality.
In Chapter 4, we presented a method that uses alternation data to add new entries
to an existing bilingual valency dictionary. The new entries have detailed information
115

about argument structure and selectional restrictions. If the existing lexicon has only
one half of the alternation, then our method constructs new Japanese entries with 69%
of the verbs having one or more correct entries. We also showed that it is possible tosimultaneously add entries to a second language with a reduced accuracy of 56% ifyour exisiting lexicon has such information. In this section we focused on one class ofalternations, but it is applicable to any alternation.
In Chapter 5, we showed that sense-based semantic features combined with ontological information are effective for parse selection. Training and testing on thedefinition subset of the Hinoki corpus, a combined model gave a 5.6% improvement inparse selection accuracy over a model using only syntactic features (63.8% → 69.4%).
Similar results (76.2% → 79.2%) were found with example sentences.
In Chapter 6, to get sense information automatically, we proposed a method forword sense disambiguation (WSD) using superordinate semantic classes. We separated
this method into two stages. In the first stage, we estimate superordinate semantic
classes. We did this using CRFs, and were able to disambiguate with a very highaccuracy.
In the second stage we estimate word senses using the results of the first stage.
We got higher accuracy for WSD than published best method of Japanese dictionarybased lexical-sample task of SENSEVAL-2. In addition, we showed the effectivenessof superordinate semantic classes for unseen words.
As shown above, though the most recent research direction is on statistical methods, rich resources (dictionary, ontology, treebank, sensebank, etc.) are effective fordeeper natural language processing. We showed the effectiveness through task-based
evaluations, machine translation, parse selection and word sense disambiguation.

7.2 Future Work
There are several directions for future research.
Construction of Rich Information Resources Construction of Rich Information
Resources In this thesis, we introduced several manual or semi-automatic methods ofconstructing rich information resources. Especially for the bilingual valency dictionary, we investigated effective methods to expand it.
116

In future work, we want to expand the resources in several ways. For Lexeed, wewant to import entries or senses from other machine readable dictionaries or online
resources such as Wikipedia1 and Wiktionary2 . From these we can extract at least alemma and it’s definition. Then, by using link information of online dictionaries, we
can extract other information such as examples, frequency, and access frequency.
For Hinoki, we plan to expand the target domain into open domains such as Blog,e-mail. We have already begun to expand the sensebank over blog data. We illustrate
the rough plan of expanding the target domain in Figure 7.1.

Figure 7.1: Plan to Expand Resources: from closed world to open domain, from handbuild to semi-automatic

For Goi-Taikei, we want to reliably add words to semantic classes. One way is touse parse results of definitions in other dictionaries like Bond et al. (2004). Now, weare extracting unknown words from Wikipedia, then trying to estimate the semantic
1 http://en.wikipedia.org/wiki/Main Page
2 http://ja.wiktionary.org/wiki/117

classes through a rule based method. On the other hand, NTT is planing to make
Goi-Taikei open. We can then expect to improve Goi-Taikei’s coverage and keep it
up-to-date by getting users feedback.

Usage of the Resources As shown in Chapter 5 and 6, we are combining symbolicand statistical approaches to parse selection and word sense disambiguation. In future
work, we want to go ahead with combining approaches to more various natural language processing tasks; especially for machine translation. In this thesis, we used therule-based machine translation system ALT-J/E, but great progress has been made inlearning statistical models from annotated corpora, and some (online) statistical machine translation systems3 are now available. However, statistical machine translation
is not strong for out of domain data. According to Koehn and Monz (2006), for indomain data, statistical approaches are stronger, but for out-of-domain data, rule-based
system Systran4 becomes stronger. It shows that because dictionaries and translation
rules are relatively domain independent, they help to make systems robust. So in future work, we want to export the bilingual valency dictionary into a statistical machinetranslation system: we need to investigate the best way to export the dictionary.
We also intend to make a superordinate semantic class tagger. If we can provide
a packaged Goi-Taikei semantic class tagger, it will help the open Goi-Taikei to gain
wide acceptance. Finally, we intend to confirm that we can get improved results with
raw sense disambiguation results not just the gold standard annotations.

7.3 Conclusion
In this thesis, we first introduced various rich information resources which we haveused or constructed: Goi-Taikei, its bilingual valency (pattern) dictionary, Hinoki, andLexeed. We also compared the these resources with other similar resources. Because
they were mainly built by hand, constructing such rich resources was both time consuming andcostly. To extend such rich resources efficiently, we proposed some methods to extend them using the hand-made rich resources as seeds.
3 http://www.google.com/language tools
4 http://www.systransoft.com

118

First, we proposed various methods to extend the valency dictionary: using simpler
bilingual dictionaries and linguists analyses of alternations. This not only extended but
also added more information into the bilingual valency dictionary. The evaluation of
the extended resource’s quality was done with both a translation task-based evaluation
and a direct evaluation by lexicographers. Through these evaluations, we showed theeffectiveness of our methods.
We then investigated the usage of rich information by applying it to parse selection(ranking), and to word sense disambiguation. Through these experiments, we showed
the importance and usefulness of semantic information in statistical approaches to natural language processing tasks.

119

120

Appendix A
Data on the distribution of Goi-Taikei’s
Semantic Classes
We show some data about Goi-Taikei’s classes in Tables A.1, A.2, A.3, A.4 and A.5.
Table A.1 shows that the most frequent 30 classes in Goi-Taikei’s Japanese Word
Dictionary. As shown in Table A.1, Goi-Taikei has a lot of entries about names ofplaces or humans.
Table A.2 shows that the distribution of semantic classes over newspaper text. In
the Hinoki project, all words of the first half of Kyoto Corpus (newspaper text, including 19,013 sentences, 522,884 words, 298,974 contents words) are tagged with GoiTaikei’s semantic classes (for more details of Hinoki, see Section 2.4.2). In the caseof this corpus, we tagged all contents words (not only common noun but also proper
nouns, verbs, adjectives and adverbs) using Goi-Taikei’s common noun ontology. But
in Table A.2, we showed the nouns only.

121

Table A.1: Most Frequent 30 Semantic Classes in Japanese DictionaryClassh464:jurisdictioni
h48:male/mani
h5:humani
h471:landi
h49:female/womani
h459:zone/area/districti
h364:executive agency/
administrative bodyi
h414:stationi
h495:rivers and streamsi
h499:wetlandsi
h1035:methodi
h413:platform/loading platformi
h973:electrical componenti
h465:cityi
h2498:structurei
h374:enterprise/
corporation/industryi
h1020:logici
h2595:uniti
h428:work placei
h712:matter/material (bodies)i
h2586:numberi
h2596:calculated valuei
h2435:pattern, methodi
h2591:weights and measuresi
h971:computeri
h367:public institutioni
h1008:knowledge, intelligencei
h962:machineryi
h2592:degree/extent/measurei
h507:sea/oceani

Lvl
4
7
4
6
7
4
5

Token
93,14138,79829,654
17,855
11,846
8,411
7,567

Sample Word
toshi “city”
dansei “male”
hito “person”
tochi “land”
josei “female”
chiiki “region”
seifu “government”

6
7
7
6
5
7
4
5
6

5,876
5,094
5,091
4,454
3,854
3,546
3,532
3,338
2,669

²· hômu “home”
HB suikei “water system”¡Ç koshou “lake”
M taisaku “action”
¶¡Â tâminaru “terminal”*â teikou “resistance”
bm juutaku “houce”
jp seido “”DÝ kigyo “company”

6
4
4
5
4
4
5
5
7
5
6
5
5
6

2,248
2,084
2,059
1,805
1,787
1,753
1,705
1,656
1,613
1,550
1,529
1,394
1,359
1,219

30& tetuzuki “procedure”
Í ichibu “part”
 kaisha “company”
 bushitu “matter”
Í= hitotu “one”
÷ kinri “interest rate”
Kj taisei “system”
Td saidai “maximum”¨Ê pasokon “personal computer”
Ñà byouin “hospital”
õ` jouhou “information”
· shisutemu “system”
. takasa “hight”
 umi “sea”

122

k¿
n
0
qª
n
ªÉ
pë

Table A.2: Top 30 Semantic Classes over Newspaper Text (first half of Kyoto Corpus):
Noun OnlyClass
Lvl
Token Sample Word
h2586:numberi
4 13,805
28 “28”h2595:uniti
48,201
nen “year”
h47:men and women/genderi
67,259
Murayama “family name”
4
6,064
Kouch “Kouchi Prefecture”
h464:jurisdictioni
h385:nationi
45,959
roshia “Russia”
h2682:dayi
6
3,018
nichi “sun”
6
3,006
koto “thing”
h1022:circumstance/thing/matter/affairi
h48:male/mani
7
2,757
Tomiichi “name”
h374:enterprise/
6
2,339
kaisha “company”
corporation/industryi
h459:zone/area/districti
42,153
nanbu “south”
h380:political partyi
62,093
shakaitou “Socialist Party”
h260:politiciani
7
2,079
shusho “Prime Minister”
h2535:aspect/condition/phase (other)i
5
1,755
kanou “possibility”
6
1,753
shi “Mr.”
h43:honorific title/term of respecti
h364:executive agency/
5
1,738
naikaku “cabinet”
administrative bodyi
h1680:sporti
6
1,715
sakkâ “soccer”
6
1,494
kotoshi “this year”
h2679:yeari
h2680:monthi
6
1,443
nigatu “February”
5
1,205
you “like”
h2509:circumstance/situationi
h2600:parti
5
1,122
bu “division”
h363:establishment/institutioni
4
1,070
gikai “assembly”
h465:cityi
4
1,033
shuto “capital”
h2508:aspect/condition/phasei
4
1,028
teki “target”
5
1,014
houshin “policy”
h2456:purposei
h2692:the timei
6904
gozen “morning”
6895
jiki “season”
h2695:period (natural andhuman activity, etc.i
h378:societyi
6894
kai “meeting”
h323:chief/president/manageri
6893
gichou “chairperson”
5874
jyûdai “important”
h2608:extent/degreei
h2623:interiori
6841
naibu “inside”
Total (Noun)- 213,276

èd℄

9
©
Äy
ñ
,A
è¿


ã
u
<
a
Ì
×


0
èa
a

{
<k
A
j.
²Ï
éU

{ò
ld
×

123

Tables A.3, A.4 shows that the distribution of semantic classes merged into superordinate semantic classes at level 2 and 3. In Tables A.3, A.4, we don’t restrict by
it’s POS: that is they are including all contents words. Tables A.3, A.4 shows that thesemantic classes are heterogeneously-distributed. From Table A.3, h1235:eventi andh2422:abstract relationshipi appear at a high rate. And Table A.4 shows thatthe majority of children of h1235:eventi is h1236:human activityi.
Table A.3: Distribution of Semantic Classes in Newspaper Text (The first half of Kyoto
Corpus): Merged into Superordinate Semantic Classes at Level 2Class
h1:common nouni
h2:concretei
h3:agenti
h388:placei
h533:objecti
h1000:abstracti
h1001:abstract thingi
h1235:eventi
h2422:abstract relationshipi
Total

Lvl

Token

(%) Sample Word

0
46
0
1
81
0
2 49,248 16.5
2 16,058 5.4
2 10,581 3.5
14
0
2 19,382 6.5
2 96,551 32.3
2 107,023 35.8
298,974124

100

e,e arekore “this and that”Ç banbutsu “all things”
Ï watashi “I”§; honjin “headquarters”Ê À gondora “gondola”[G mono “thing”
÷È jourei “regulation”
÷ ninkan “appointment”+¶ haigo “back”

Table A.4: Distribution of Semantic Classes in Newspaper Text (The first half of Kyoto
Corpus): Marged into Superordinate Semantic Classes at Level 3
Class
Lvl
Token (%) Sample Wordh1:common nouni
0
46
0
arekore “this and that”
1
81
0
banbutsu “all things”
h2:concretei
h3:agenti
2
318 0.1
shutai “subject”,
h4:personi
3 29,785
10
watashi “I”
h362:organizationsi
3 19,145 6.4
cheko “Czech”
h388:placei
2
163 0.1
tokoro “place”
3
3,918 1.3
honjin “headquarters”
h389:facilityi
h458:regioni
3 10,574 3.5
nanbu “south”
3
1,403 0.5bı̂chi “beach”
h468:natural placei
h533:objecti210mono “thing”
3
2,148 0.7
kingyo “goldfish”
h534:animatei
h706:inanimatei
38,432 2.8
boirâ “boiler”
1
40
mono “thing”
h1000:abstracti
h1002:mental thingi
3 12,534 4.2
chi “wisdom”
h1154:abstract thing (behavior)i
3
6,848 2.3
jourei “regulation”
h1235:eventi
2
286 0.1
7fushigi “seven wonders”
h1236:human activityi
3 68,067 22.8
keshi-tomeru “put out”
3 24,757 8.3
aratamaru “be renewed”
h2054:phenomenai
h2304:natural phenomenai
33,441 1.2
kusaru “go bad”
2
50
zure “difference”
h2422:abstract relationshipi
h2423:existence/beingi
3
3,105
1
ryûho “reservation”
32,433 0.8
joui “higher rank”
h2432:kind OR systemi
h2443:connected to/related toi
3
9,107
3
kougo “alternation”
h2483:nature/dispositioni
34,454 1.5
fukai “profound”
3 31,134 10.4
shinkû “vacuum”
h2507:statei
3
397 0.1
eikaku “acute angle”
h2564:shapei
h2585:amounti
3 32,459 10.9
aredake “that much”
h2610:locationi
34,949 1.7
haigo “back”
3 18,980 6.3
shûkan “week”
h2670:timei
Total
298,974 100

e,e
Ç
0K
Ï
~
A,f
§;
ã
ª

÷²
³{À
[G
©
÷È
ãÂ{
È0ÊZd
Wd
d
3e
J
ê­
°

"
Ì
e9*
+¶
\/

125

Of Goi-Taikei’s classes, 547 classes don’t appear in the newspaper text (The firsthalf of Kyoto Corpus). We show some samples of semantic classes which don’t appear in that newspaper text in Table A.5. Table A.5 lists the top 10 classes in order ofthe number of tokens in Goi-Taikei’s Japanese dictionary. Note that some classes ofthese classes have children which appear in the Corpus. Forexample, h963:generalmachineryi doesn’t appear in the target text, but its children h964:motori, h965:implementi, h966:communicatori and h967:machine parti appear 23 times collectively. Therefore, we marked Table A.5 whose children appear or doesn’t appear. In
the column Children Appear, Yes means that children of the class appear, and No meansthat children of the class doesn’t appear.

126

Table A.5: Samples of Semantic Classes which don’t appear in Newspaper Text (The
first half of Kyoto Corpus): Top 30 classes in Goi-Taikei’s Japanese DictionaryClassh504:springs and wellsi
h1082:sentencei
h726:charcoali
h791:mineral oil/petroleumi
h165:servant/retainer/employeei
h213:good person/
virtuous personi
h978:optical componenti
h1529:explanatory notesi
h966:communicatorih200:lazy personi
h1039:poetryi
h963:general machineryi
h1243:madnessi
h704:bark, peel/rind/skinih315:prostitutei
h900:ovenih164:feudal lordi
h628:mole, warti
h206:flirt/a lustfuland promiscuous personi
h314:gangsteri
h196:coward/weaklingi
h290:shipping agent/carrieri
h775:stonei
h1104:figure, table, scorei
h118:companioni
h1090:Cn- and Jn-stylereadings of Cn charactersi
h1091:grapheme (linguistic)i
h2618:borderi
h333:the roles of peoplei
h699:flowerih623:membranei

Lvl Token Sample Word
(Dict.)
7437
onsen “hot spring”
7
134
bun “sentence”
8104
sekitan “coal”
795
sekiyu “oil”
8
78
shin “vassal”

Vµ
%

&
&

ChildrenAppear
Yes
No
No
No
No

8

62

 shinja “believer”

No

7
9
7
8
6
6
6
6
7
8
8
8
10

62
52
51
49
49
48
47
45
45
45
45
44
44

 sekutâ “sector”
È* chuushaku “note”°Â beruto “belt”
LyÉ kiseichu “parasite”
ü ku “phrase”
X kikai “machine”
Í[ kyouki “madness”
BÔ jushi “resin”
7|ç baishunfu “prostitute”
Y kama “oven”
Ä lodo “road”
8, tako “lump”
 sado “sadism”

Yes
No
No
No
Yes
Yes
No
No
No
No
No
No
No

7
8
8
7
7
10
7

43
43
42
41
40
39
39

ã® furyou “inferiority”. jakusha “weak”
Ä´ kyouryoku “great strength”­Ä burokku “block”
AÌ zuhyo “chart”
Â/ nakama “friend”
$

No
No
No
No
Yes
No
No

6
4
5
7
7

38
37
36
34
34

åç eijina “symbolic name”
Yes&eZ kireme “slit”
Yes
1+ kankeisha “person concerned” Yess! kafun “pollen”
No
ÿ· moumaku “retina”
No

kun “Jn reading of a Cn character”

127

128

Appendix B
Classification of English Alternations
for Japanese S = O Alternation

Japanese

Vi
EnglishJapanese

Vt
EnglishType: S = O(aku
open, be open
*d akeru open(
aku
open, become empty*d akeru open, empty
é(aku
open, be open
é*d akeru open, dawn
8d ataru touch, hit, be hit
?d ateru hit
?Wd atehamaru apply (a rule), be appli- ?Zd atehameru apply
cable

hLd

abiru

pour(over oneself), bathe=aradatsu

be aggravated, be roughor aggravated or worse

ieru

heal

itamu

hurt, be hurt

iburu

smoke

iyashimu

despiseukabu

float, float up

uku

float

'!d
Y
d
0Y
ñ$O
ñ(

hL4d

abiseru?dpour(overanother),bathe, pour onaradateru

aggravate

iyasu

heal

itameru

injure, hurt

ibusu

fumigate, smoke

iyashimeru

despiseukaberu

set afloat, float up, float

ukasu

float

'2
Zd
2
0Zd
ñ$Rd
ñ$2
129

JapaneseVi
EnglishType: S = O¥( ugoku
9d udaru½d utsuru

move
boil
move, move (a to b)

Japanese

¥$2
d
½2

d utsuru project, be photographed 2
Cd uragaeru be turn inside out
C2
7ed ureru sell (well), sell, be sold 7d
f&d okiru wake up, get up
f,2
Eed oboreru drownEb2
ed oreru breakd
Qhd owaru end, finish
Q!d
Yd kaeru return (home), go back Y2
Yd kaeru returnC2
Cd kaeru returnC2
Ø$d kakaru hang down, cost, take Ø*dVtEnglishugokasu

move

uderu

boil

utsusumove, move (a to b), remove

utsusuproject, filmuragaesu

turn inside out

uru

sell

okosu

wake up, raiseoborasu

drown

oru

break

oeru

end, finish

kaesu

return (home), send backkaesu

returnkaesu

return, return somethingkakeruhang, spend, wear

kagameru

bend, stoop

kakusu

hide

kaku

lack

kasaneru

pile up(e.g. time, money, etc),easel

Y kagamu bend, stoop
Zdâed kakureru hideâ2
Z*d kakeru lack, be lacking
Z(
lCd kasanaru pile up, piled up, be piled lFdup,+d katageru lean
Wd katamaru harden,( katamuku lean, incline towardÌ= kadodatsu be sharped kareru dry up
þ( kawaku dry, get dry,) katagu lean
Zd katameru harden,*d katamukeru lean, inclineÌ?d kadodateru be sharp
b2 karasu dry up
þ$2 kawakasu dry, dry (clothes, etc.)130JapaneseVi
EnglishJapanese

Vt
EnglishType: S = O

hd kawaru change, take the place of !d kaeru change, exchange
Uhd kawaru change, take the place of U!d kaeru change, exchange
bhd kawaru take the place of, change b!d kaeru change, exchange>hd kawaru change>!d kaeru change&&Ä kikichigau mishear&&Ä!d kikichigaeru mishear
 Uhd kirikawaru change completely
 U!d kirikaeru change
dkusaruspoil, rot
b2 kusarasu spoil
ed kusareru spoil
b2 kusarasu spoil
b*d kudakeru be smashed, breakb(kudakusmash, breakdkutsugaeru capsize, topple over2
kutsugaesu capsize, overturn ê%d kuriagaru move up (date or rank)  ê+d kuriageru move up
wd
kooru
freezewb2 koorasu freeze
Ì+d kogeru be scorched, burnÌ%2 kogasu scorch, burnÒed koboreru spill, be spilt, overflow Ò2kobosu
spill
Úd
koru
be devoted to, stiffen, Úb2
korasudevote to, stiffen, conU%d
U+d
ed
Y%d
Û*d
ËZd
ÉZd

grow stiff

U%2
korogeruroll, roll over
U%2
kowareru
break, be broken
2sagarudrop, hang down
Y+dsakerutear, be torn, splitÛ(sameruawake, wakeËW2
sameruget cool, cool, become ÉW2korogaruroll, roll overcoolS sarau review°ê%d shiagaru be finishedùY
shizumusink
ËDØ$d shinikakaru be dying
1Wd shimaru be closed, closedshimeru
get wet, be wet

centratekorogasuroll, roll overkorogasurollkowasubreaksagerulower, hang down, hang

saku

tear

samasuarouse, wake, awakensamasucool

S!d saraeru review°ê+d shiageru finish upùZd shizumeru sink
ËDØ*d shinikakeru be dying
1Zd shimeru close2
shimesuwet
131Japanese
Type: S = O

~'d
(
*d
^Y
bY
Ǒed
«ed
ÎWd
¨hd
¸Wd
+(

sugirusuku
sukeru
sumu
sumu

Vi
EnglishJapanese~-2
be transparent
*dbe transparent(
end, be settled, finish^W2
be clear, clear, clear (e.g. bW2
pass

Vt
Englishsugosumake pass, passsukerube transparent

suku

be transparent

sumasuend, settle, finish

sumasumake clear, clarify, clear

suru

rub

suru

rub

sebameru

narrowsonaeru

be furnished withsomerudye

somukeru

turn away, turn one’s
face away

weather)ǑdsurerurubǑdsebamaru get narrow, narrowÎZdsonawaru be furnished with¨!dsomarube dyed, dye¸Zdsomukuturn away, run counter to +*dsureru

rub

pd
soru
bend, be warped, warp
pb2 sorasu bend, warp
=
tatsu
stand
?d tateru raise, stand, stand up
ÔWd tamaru collectÔZd tameru collect
EeY%d taresagaru hang
EeY+d taresageru hang (a curtain)
Eed tareru drop, hang
Eb2 tarasu drop, suspendsYchidimushrinksZd chidimeru reduce, shrink, shorten
sed chidireru curl, be wavy
sb2 chidirasu curl¡dchiru
scatter, be scattered, fall ¡b2chirasu
scatter
ed tsukareru tire, get tired
b2 tsukarasu tire0(
tsuduku
continue, be continued
0*d tsudukeru continue
Wd tsubomaru get narrow, closeZd tsubomeru narrow, closeYtsubomu get narrower, closeZd tsubomeru narrow, close
V*d tokeru melt, dissolve
V$2 tokasu melt, dissolve
ÊWd tomaru stop, come to a halt ÊZd tomeru stop
dtomoruburn2
tomosuburn, light·Ynagusamu cheer up, comfort·Zd nagusameru cheer, comfort/Onarabuline up, form a line
/Rd naraberu line up
132

Japanese
Type: S = O

íd
!d
=
Ld

y!d
%ed
ºWd
Wd
Wd
9ed
sed
É!d

naru
nieru
nitatsu
nobiruVi
EnglishJapaneseíb2boil, be boiled
dboil or simmer
?dextend, be stretched, I2ring, sound

Vt
Englishnarasuring, sound

niru

boil

nitateru

boil or simmer

nobasuextend, stretch, lengthen

hayasugrowhagasupeel off, tear offhajimeru

start, begin

hamerufithayameru

hastenharasuswell

harasuclear up, clear away, dispel

hiyasucool, cool down

stretchy℄2hagareru
peel off, come unstuck %2
haeru

grow
from

ºZd
hamarufit
Zdhayamaru hasten, be hasty
Zd
hareruswell
9b2
hareruclear up, clear away, be sb2
hajimaru

start, begin

sunny
hieru

cool, cool down, grow
cold

É℄2

Wd hikumaru become lower, lower Zd hikumeru lower
*d hirakeru open(hirakuopen¨dhirugaeru wave, turn over¨2hirugaesu
wave, change
Ü%d hirogaru spread, widen
Ü+d hirogeru spread, widen
ÜWd hiromaru spreadÜZd hiromeru spread, broaden
!d fueru increase℄2 fuyasu increase!d fueru increase℄2 fuyasu increaseWd fukamaru deepenZd fukameru deepen
ǑbY fukuramu swell, expandǑbW2 fukuramasu inflate, expand, swellÆd
furu
rain, precipitate
Æb2 furasu rain upon, send
ObY%d burasagaru hang down, hang from ObY+d burasageru suspend, hangdheru
decrease, decrease (in b2herasudecrease, abate
size or number)

ß%d magaru bend, turn
&ê%d makiagaru roll up

ß+d mageru bend
&ê+d makiageru roll up
133

JapaneseVi
EnglishJapanese

Vt
EnglishType: S = O

/d
RWd
d
Ï(
*d
!d
d
öed
Ë*d
b)
9d
$Y

5d
matomaru be settled, conclude, be col- RZdmazarumix, be mixed
lectedmawaruturn, go roundmuku

face, point towards

2
Ï*dmazerumix, be mixed

matomeru

settle, conclude, put
in ordermawasuturn, turn roundmukeruface, point towards,
turn towards

( muku peel
moeru
burn
℄2 moyasu burnmodorureturn, turn back2 modosu return, restore
moreruleak, leak out
öb2 morasu let leak, leakyakeruburn
Ë( yaku burn, bakeyawaragu
softenb+d yawarageru soften
yudaruboil
d yuderu boil
yurumuloosen, be loose, become $Zd yurumeru loosen
mukeru

peel

loose

Ped yureru shake8hd yokotawaru lie down
.Wd yowamaru weaken, abate
( waku boil6( waku gush
Éd wataru extend
ßed wareru split, break

Pd yuru shake8!d yokotaeru lay down, lie down
.Zd yowameru weaken
$2 wakasu boil
6$2 wakasu gush
É2 watasu extend
ßd waru split, break, divide

Type: Passive

O%d
£Wd
_Wd
Ed
Wd

O+d
atatamaru be warmed, get warm, warm £Zdagaru

be fried, riseoneself

atsumaru

gather, be gatheredamaru

remain, be left

aratamaru

be renewed

_Zd
E2
Zd
134

ageru

fry, lift

atatameru

warm

atsumeru

collect, gather

amasu

save, leavearatameru

renew, changeJapaneseVi
EnglishJapanese

Type: Passive

ed
¤Y
t=
¯[ed
d
¯Wd

areru

be devastated, be stormy

itamu

be damaged

iradatsu

be excited, be irritateduzumoreru

be buriedutsurube reflectedumaru

be buried

Vt
Englishb2 arasu devastate, lay waste¤Zd itameru damaget?d iradateru excite, irritate¯Zd uzumeru bury
2 utsusu reflect, project¯Zd umeru bury, bury (e.g.
one’s face in hands)

7 ed urikireru be sold out
7 d urikiru sell outuruou
be moistened
2 uruosu moisten
hd uwaru be planted
!d ueru plantìWd osamaru be ruled, be at peaceìZd osameru rule, govern
FWd osamaru subside, be obtained
FZd osameru suppress, obtain
Wd osamaru be reached, be obtainedZd osameru reach, obtainØ(odoroku
be surprised
Ø$2 odorokasu surprise#O.d obusaru be carried#O obuu carryRdkaeru
be hatched
R2 kaesu hatch
ed kasureru be grazed
d kasuru graze?ä( kataduku be in order, be tidied, put in ?ä*d katadukeru tidy up, tidy
tY
é,!d
&,!d
[Wd
wWd
Wd

order

ÝZd
kikoeru
be heardé(kikoeru
be audible, be heard&(
kimarube decided[Zdkiyomaru become pure, be purified
wZd
kiwamaru go to extremes, be mastered, Zdkaramutwine round, be tangled

karameru

bind, tangle

kiku

hear

kiku

hear

kimerudecidekiyomeru

purifykiwameru

attain, master, taketo limit, carry to extremes

kujikubreak, crush

kumorasu

cloud

reach limit, terminate

P*dÓd

kujikeru

be discouraged, be crushedkumorube clouded

135

P(
Ób2

JapaneseVi
EnglishJapanese

Type: Passive(dWd kurumaru be wrapped up
Ì%ed kogareru be scorched,Ced konareru be digested
hed kowareru be broken
ß*d sakeru be separated´.d sasaru stick into, be stuck, stick

^Y
Ì%2
,C2
h2
ß(
´2

#Wd

#Zd

*d
ùWd
2Wd
Ê=
4

sadamaru

be decided, be fixed, become
settled(shimaru
be constricted, be strangled ùZd
shimarutigheten, be tied, be shut
2Zd
sodatsu
grow up, be brought up, raise Ê?dsabakerusorou

be sold, be in ordermatch, be arranged, becomecomplete

$d tasukaru be saved, be rescuedWd tamaru be accumulated, collectéY tawamu be bent:'ed chigireru be torn off
sWd chidimaru shrink, be shortened¡b$d chirakaru be scattered, be in disorder
Wd
Wd
$d
ä(

4!d

be caught

pickledtsuku

adhere to, be attached, catch
fire, adjoin

kurumuwrap

kogasuscorchkonasudigestkowasu

break

saku

separatesasu

thrust into,
piercesadameru

decide, fix

sabakusell, handle

shimeru

constrict, strangle

shimerutighten, tie

sodateru

bring up, be broughtupsoroeru

arrange, put thingsin orderstick,

*d tasukeru save, rescue, help
Zd tameru accumulate, amass
éZd tawameru bend:'d chigiru tear, cut up finesZd chidimeru reduce, shorten¡b$2 chirakasu scatter, scatter!dtsukamaru be caught
Ytsukaru
be soaked in, be soaked, be*dtsukamaru

Vt
Englishä*d136

aroundtsukamaeru

catchtsukamucatch

tsukerusoak

tsukeru

attach, turn onJapaneseVi
EnglishJapanese

Type: Passive

Xhd

tsutawaru

be handed down, be conveyed

X!ded tsubureru be crushed, be smashed2
Wd tsumaru be stuffed, be blocked
Zd
Y
tsumu
be stuffed, become fineZdq
totonou
be preparedq!dNbhed torawareru be caught
Nb!d*d torokeru be bewitched
$2
ìd
naoru
be cured
ì2
÷d
naoru
be fixed, be cured
÷2
Þ(Cd nakunaru be lost, disappear
Þ(C2Y
nayamube distressed, be worried
W2
Wd nitsumaru be boiled down
Zd
e*d nukeru come off, be thrown, come e(
#Ld
íd
«ed
D&¥O
V%d
9Y
Ñ8d
#ed
qed

out
nobirube prolonged

noru

ride, be placed on, get on

hazureru

come off, be disconnected

#I2
í4d
«2

tsutaeru

conveytsubusucrush, smash

tsumeru

stuff, block, packtsumeru

stuff, packtotonoeru

prepare, put in order

toraerucatch

torokasu

bewitch

naosu

cure

naosu

repair, curenakunasu

lose

nayamasu

distress, afflictnitsumeru

boil down

nuku

remove, throw, extract

nobasuprolong, lengthen

noserugive a ride, place on

hazusutake off, disconnect,
unfastenD&¥I2 fukitobasu blow off
fusagaru
be blocked, be plugged up
V)fusagublock, stop up
hekomubecome hollow, be dented
9W2 hekomasu dent
hedataru
be distant, be separated
Ñ?d hedateru separate
magireru be diverted#b2 magirasu divert
makureru be tucked up, be turned up qd
makurutuck up, verb suffix
fukitobu

be blown off(inside out)

ø*d
WOed

Vt
Englishmakerube defeated, losemabureru

be smeared

ø$2
WO2
137

to indicate recklessabandonmakasudefeatmabususmear

JapaneseVi
EnglishJapanese

Vt
EnglishType: Passive

ë
|!d
wed

ëh2
mieru
be seen, be visible|dmidareru be disorderd, be disor- w2
mayou

be puzzled, be lost

mayowasu

puzzle, lose

miru

see

midasuput in disorder, disorder,
throw out of orderdered, get confused

É:d michiru be filled, be full
É82 mitasu fill, satisfy|ä$d mitsukaru be found|ä*d mitsukeru find, be familiar
ýed mureru be steamed, be stuffy ý2 musu steamýed mureru be steamed, be stuffy ýb2 murasu steam, cook by steamÕZd momeru be wrinkledÕY momu wrinkle
Wd yasumaru be restedZd yasumeru set at ease, rested yabureru get broken, be torn, get d yaburu break, tear
'ed
7ed
GO

torn

'd
yogoreru be stained, get dirty, be- 72
yabureru

be defeatedcome dirty, be dirty
yorokobubedelighted,
pleasedbeGI2

$ed wakareru branch off, be divided *d wazurau be worried, worry h2yaburudefeatyogosustain, disgrace, soil, dirty

yorokobasu

please, delightwakerudividewazurawasu

worry, trouble

asobasu

let play, let one playikasu

revive, make live

kagayakasu

make shine, light up

kabuseru

cover

karamuentwinekarasumake hoarse, exhaust, letwither, let dry

about

Type: Synthetic

>O
y&d
h(
.d
tWd
ed
¤ä(

>I2
ikiru
live
y$2
kagayaku shine
h$2
kabusaru get coverd
4d
karamaru become entwinedtY
kareruget hoarse, dry up,
b2
asobu

play

witherkizutsuku

get hurt, be hurt

¤ä*d kizutsukeru hurt, wound138

JapaneseVi
EnglishJapanese

Type: Synthetic

Wdshizumaruåd

get quiet, quieten, quieten down

suberuslip, glide

tooru

pass, go through

tobu

fly

naku

cry

d
¥O
(

Zd

shizumeru

quiet, calm, appeaseåb2suberasu

let slip, let something
slip

toosu

let pass, allow throughtobasulet fly, skip overnakasumake cry, make someone
cry

nakusulose, remove, lose something

2
¥I2
$2

Þ(Cd nakunaru get lost, lose, disappear Þ(2
àed
+d
vd

Vt
Englishàb2
%2
become v2

narerubecome domesticatednarasudomesticate

nigeruescapenigasulet escape, let loose

nigoruget muddy,
muddy

nigosumuddy, make muddy

VWd nukumaru get warm
V[d nukumoru get warmûed nureru get wet
d neru sleep, go to bed
ed nogareru escapeÈd hikaru shine
Ǒed fukureru swellN℄*d fuyakeru get soaked
6Wd marumaru be roundè= motsu haveöd moru leak
Ped yureru swayÏed yojireru get twistedb wazurau worry

VZd nukumeru warm
VZd nukumeru warm
ûb2 nurasu wet, dampen
$2 nekasu make sleep, put to sleep
2 nogasu let escape, let looseÈb2 hikarasu make shineǑbW2 fukuramasu make swellN℄$2 fuyakasu soak
6Zd marumeru make roundè82 motasu let haveöb2 morasu let leak
Pd%2 yurugasu make swayÏd yojiru twistbh2 wazurawasu make worried
139

JapaneseVi
EnglishJapanese

Type: Diff Head

Vt
English

aumatch, fit

h2

awasu

ÙG(

aonokulook upÙG*dbring together, join together

aonokeru

turn up (one’s face or acard)ÙÏ( aomuku look upward
ÙÏ*d aomukeru turn up (ones face)
bY akaramu become red
bZd akarameru blushª%d agaru rise, become prosperous ª+d ageru raise
ê%d agaru rise, enterê+d ageru raise, give{&d akiru be fed up, get tired of {$2 akasu weary, glut
é*d akeru dawn
é$2 akasu spend(the night)
!d amaeru fawn upon℄$2 amayakasu spoil
hed arawareru appear
h2 arawasu showÌhed arawareru appear
Ìh2 arawasu express
h.d awasaru get together
h2 awasu join together
h.d awasaru get togetherh4d awaseru join together(.d awasaru get together(4d awaseru unitem= awadatsu bubblem?d awadateru beatsd
ikaru
get angry
sb2 ikarasu anger someone
Õd
itaru
reach
²2
itasu
bring about.d iyamasaru become still greater 2 iyamasu increase (all the more)óebhd irekawaru change places
óeU!d irekaeru replaceóeÄ irechigau pass each otheróeÄ!d irechigaeru misplace>$d ukaru pass(an exam), pass >*d ukeru take(an exam), underIWd
4d
(
(
d

usumaru

become thin,
weak

useru

disappear

utsumuku

look down

utsumuku

look downward

utsurube reflected

become

IZd

(
*d
2
140

take
usumerumake thin, diluteushinau

lose

utsumukeru

cast down

utsumukeru

turn upside down

utsusucopy

Japanese
Type: Diff Head

yWed
.ä(
´ed
f,d
Òd
KWd

umareru
endukuokureruokoru
okoru
osamaru

Vi
EnglishJapanese

yY
marry
.ä*d
be late for, be late´b2happen, occur
f,2
rise
Ò2
be controled, govern KZd
be born

Vt
Englishumu

give birth

endukeru

marry offokurasudelay, retard

okosu

arouse, raiseokosu

reviveosameru

control, study

oneself>0Wd oshitsumaru approach the year end >0Zd oshitsumeru pack (in box)
Êhd osowaru learn
Ê!d oshieru teachu:À( ochitsuku calm down
u:À*d ochitsukeru quiet
u:d ochiru fall, fail (e.g. exam) uA2 otosu drop
Ñ!d obieru be frightend
Ñ$2 obiyakasu threatenÂomoioccur toÂomoibe reminded of
ñ$O ukabu
ñ$Rd ukaberu
O
oyobu
reach
U2 oyobosu influence lCd orikasanaru lie on top of one another  lFd orikasaneru fold backY d oriru get off, go down,
Yf2 orosu let off, lower,
o$d
o*
Ñ8d
*d
ë
tWd
È!d
(
Àd
ed



alight (e.g. from bus)
kakarube suspended fromkake-far apart

hedataru
kakerugallopkanau

come true

karamaru

twine roundkieru

go out, disappearkiku

be effective

kiru

wear

kirerube cut off

kuiau

fit together

kuu

eat

o*d kakeruo*kakeÑ?d hedateru
dkaru
ë!d kanaeru
tZd karameru
È2kesu
$2 kikasu
À4d kiseru
b2 kirasu
h2 kuiawasu
h2 kuwasu
141

take downhang
put distance
betweenspur ongrant (request, wish)bind
extinguishusedress, put on clothes
run short ofclenchfeedJapanese
Type: Diff Head

(2Od kusuburu
eed kuzureru
Yd kudaru

Vi
EnglishJapanese(2Rdcollapsee2
go down, descend, get Y2
smoke

Vt
Englishkusuberu

fumigatekuzusudestroykudasu

lower

down

Yd kudaru go down
Y.d kudasaru bestow(<=( kuttsuku adhere to(<=*d kuttsukeru attach
Wd kubomaru be low (as a hollow) 9Zd kubomeru hollow outþY kuramu grow dizzyW2 kuramasu dazzleÍ kuruu go madÍh2 kuruwasu drive mad0Y kurushimu suffer
0Zd kurushimeru torment, afflict(dWd kurumaru be wrapped up(dZd kurumeru lump together$Wd kuromaru blacken$Zd kuromeru make something black`hd kuwawaru join, join in`!d kuwaeru add, add to, append
!d koeru get fat, become fertile, ℄2 koyasu fertilize, make fertile
grow fat,1ed kojireru get worse,1b2
ä$d kotodukaru be entrusted with
ä*d
áL4d kotoyoseru pretendáL2ù[d komoru be full of-Zdá d koriru learn by experience áb2
= sakadatsu bristle, stand up
?d©= sakidatsu lead©?dA$d sazukaru receive, be giftedA*d) sawagu be excited
%2e shitagau go along withe!d
©ed shireru become known©dÌed jireru be impatient, get impa- Ìb2ä( suitsuku
( sukutient
stick tobe transparent

kojirasu

aggravatekotodukeru

send word

kotoyosu

find an excuse

komeruincludekorasuchastise

sakadateru

ruffle up, ruffle

sakidateru

have go ahead

sazukeru

grantsawagasu

agitateshitagaeru

take along withshiru

come to know

jirasuirritateä*d suitsukeru attract
$2 sukasu look through142

JapaneseVi
EnglishJapanese

Vt
EnglishType: Diff HeadYsukumucrouch, cowerZd sukumeru duck(head), shrug
*d sukeru be transparent
$2 sukasu look through-Ysusumumake progress-Zd susumeru advance ê%d suriagaru be off the press ê+d suriageru finish printingǑ ed surikireru wear outǑ d surikiru cut by rubbing« ed surikireru wear out« d surikiru cut by rubbingǑ *d surimukeru abradeǑ ( surimuku skin (one’s knee)Odsuwarusit, squat[!d sueru set
3ed zureru slip, slide
3b2 zurasu shift, put offQ
sougo along with, accom- Q!dsoeru
add to
+d
!d
ed
¥!d
ted

pany

sogerusobieru
soreru

)
rise
℄$2
deviate, stray from sub- b2
be hollow

ject
taeru

die out

taoreru

fall down, collapse

Wd takamaru rise:ê%d tachiagaru stand up
m=
tatsu
stand
öä( chikaduku approach
öLd
´
ä(
Ä
!d
¢!d
³!d

chikayoru

approachchikara-by force

duku
chigaudeviate, differ

tsuieru

collapse

tsuieru

collapsetsukaeru

be usable

¥℄2
t2

sogu

diminishsobiyakasu

raisesorasudivert, turn away

tayasuexterminatetaosu

bring down,
knock
down, throw downZd takameru raise
:ê+d tachiageru boot (a computer)m?d tateru build
öä*d chikadukeru allow to come near, bring
öL4d
´
ä*d
Ä!d
¢℄2
¢℄2
³
143

near
chikayoseru

bring close tochikara-encourage

dukeruchigaeru

break (one’s word), alter,changetsuiyasu

waste

tsuiyasu

spend

tsukauuse

JapaneseVi
EnglishJapanese

Vt
EnglishType: Diff Headtsugaumate
!d tsugaeru fix (an arrow to a string)Â&´.d tsukisasaru stick into
Â&´2 tsukisasu stabÂ&d tsukitooru penetrate
Â&2 tsukitoosu pierce
I(
tsuku
settle in
I*d tsukeru put
À(tsuku
arrive at
À*d tsukeru arriveÂ<= tsuttatsu stand up
Â<?d tsuttateru stab
Wd tsudumaru shrink
Zd tsudumeru reduceåWd tsutomaru be fit foråZd tsutomeru serve
ÜWd tsutomaru be fit forÜZd tsutomeru act as
E%d tsunagaru be tied together
E+d tsunageru connect
Xtsumiaccumulate
Xtsumibe piled up
lCd kasanaru
lFd kasaneru
ÄWd tsuyomaru grow strong, get strong ÄZd tsuyomeru strenghten, strengthen
åCd tsuranaru lie in a row, extend
åFd tsuraneru line up, linkÍed tereru be shy
Íb2 terasu shine on
wd
deru
come out, go out
w2
dasu
remove, send out
2/$d toozakaru withdraw, go far off 2/*d toozakeru shun, keep away2℄( toonoku become distant2℄*d toonokeru keep at a distance
A%d togaru become sharp®b2 togarasu sharpen
Ä(
todokuarrive, reachÄ*d todokeru deliverWd todomaru be fixedZd todomeru stop
DWd tomaru stay(at), stay, stay at (e.g. DZd tomeru lodge, give shelter to
èY
Wd
[ed
1ed
℄(
ed

hotel)
tomu

grow rich, be richtomoruburntorerube produced

torerucome off

doku

retreatnagareru

flow, stream

èW2
W2
[d
1d
℄$2
2
144

tomasuenrichtomosu

light

toru

take

toru

take

dokasuremovenagasuwash away, pour, drain

JapaneseVi
EnglishType: Diff HeadJapanese

Vt
English(Cdnakunarudie(2nakusu(Cdnakunaru

die

(C2

lose someone,lose
someone, wife, child, etc

nakunasu(natsuku

become attached to, be
attached, become emotionally attached*dlose someone,
child, etc

natsukeru

win over, gain affection

nabikubendnabikasu

seducenarasutame, accustom

niowasugive out an odor, scent or
perfumenigiwasu

make prosperous

nigosumake muddy

niburasu

blunt

niseruimitate, copy

nukasuleave out, omit

nugutake off, take off clothes

nurumeru

make less hot

nejiruscrew

nekaseru

put to sleep

nokeruexpel, repel, remove

nokosuleave, leave over, leave(behind, over)

N(
ed
ì
íh
v-d
Ôd
âd
e*d
+d
VY
1ed
d
℄(
¯d

nareru

N$2
get used to, grow accus- b2
tomed toniou

be fragrant

nigiwau

prospernigoruniburuniru
nukerunugerunurumunejireru
neru
noku

íh2
become muddy
v-2
become less capableÔb2
resembleâ4dcome off, come out
e$2
come off)
become lukewarm
VZd
twist
1d
sleep
4d
retreat, get out of the ℄*dway

nokoruìh2

remain¯2

0ê%d noshiagaru stand on tiptoe
0ê+d noshiageru promoteidnoru
appear in print, appear i4d
noserupublish, place onódH)ed(in print)
hairu

enter

hagureru

go astray

óed ireru put inH)b$2 hagurakasu put off
145

wife,

JapaneseVi
EnglishJapanese

Type: Diff Head

+d
+d
ðFCd
Þ ä(
T ä(
Ied

hagerucome off

hagerucome offhanekaeru

reboundharitsuku

cling

Vt
English%2 hagasu peel off, tear off)hagu
peel off, tear off
ðFC2 hanekaesu rejectÞ ä*d haritsukeru attach to a flat surface
with glue

haritsuku

bareruÞ ä*d haritsukeru attach to a flat surface

cling
come to light, leak out (asecret)Ib2

with glue
barasuexposeÛ&Y%d hikisagaru withdraw
Û&Y+d hikisageru pull down
Û&2Wd hikishimaru become tense
Û&2Zd hikishimeru tighten
Û&= hikitatsu become active
Û&?d hikitateru favour
Û<Ø$d hikkakaru be caught inÛ<Ø*d hikkakeru hang on
K<( hikkuri- be overturnedK<( hikkuri- turn over
Cd
kaeru
C2
kaesu
Û<-Y hikkomu draw back
Û<-Zd hikkomeru pull back, draw inÛ<-Y hikkomu withdraw
Û<-W2 hikkomasu pull backÍ(hirameku
flash (of thunder)
Í$2 hiramekasu brandish
1-Y fuujikomu entrap
1-Zd fuujikomeru shut inD(
fuku
blow, blow (wind, etc)
D$2 fukasu puff, smoke (a cigarette)
ý*d fukeru become ready to eat (as a ý$2 fukasu steam
2
Æ -Y
/
O=$d
T)ed
fd
Ǒ*d

result of sfusu

lie down

furikomurain upon

4d fuseru lay down
Æ -Zd furikomeru rain (or snow), keeping/!dbutsukaru
appear, hit
O=*dhogureru
come untiedT)2
hosorubecome thinfZd
hodokeru
loosen, come untied, Ǒ(
furuu

shake

come apart146

people indoor
furuerushiverbutsukeru

display, strike

hogusuuntie

hosomeru

make narrow

hodokusolve, untie, take apart,
unfastenJapanese
Type: Diff Head

℄Z(
ðLd

U*d
&ä(
ªd
/Ä

honomeku
horobiru

Vi
EnglishJapanese℄Z$2
go to ruin, perish, be ru- ðU2
be seen dimly

honomekasu

hint athorobosu

destroy

inedA2bokasushade offmakitsuku twine around&ä*d makitsukeru wreathe (e.g rope)matagaru
sit astrideª)matagustraddle
machigaube wrong, make a mis- /Ä!d machigaeru mistake, make an error,
bokeru

fade

take

err


madou
be puzzled
|d
miru
see
^L
musubibe connectedä(
tsuku
or related
$d moukaru be profitable[+d mogeru come off
è:ê%d mochiagaru liftz ê%d moriagaru rouseË&ä( yakitsuku scorch
= yakudatsu be useful
pd
yadorulodge (at), lodge
Yyugamu
warp
Pd
yuru
shake
Ped yureru shakeLdyoru
approach, visit
fdwatarucross overh2 madowasu bewilder|4d miseru show^Lmusubicombine
ä*d tsukeru
*d moukeru earn, get['d mogiru pluck off
è:ê+d mochiageru raisez ê+d moriageru pile up
Ë&ä*d yakitsukeru burn or bake into
?d yakudateru put to use
p2
yadosuconceive, keep
Zd yugameru bend
Pb2 yurasu rockPb2 yurasu rockL4d yoseru let come near, collectf2
watasuhand over, pass over

Type: Diff Structureed

VtEnglishkirerube cut off, be cut, cut
well

Â&e*d tsukinukeru pierce through
Íd
teru
shine
 Ï( furimuku turn one’s face

d

kiru

cut

Â&e( tsukinuku pierce
Íb2 terasu illuminate, shine on Ï*d furimukeru turn

147

148

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List of Publications
List of Major Publications
Refereed Journal Publications
Sanae Fujita and Francis Bond. An Investigation into the Nature of Verbal Alternations and their Use in the Creation of Bilingual Valency Entries. Journal of Natural
Language Processing, Vol. 12, No. 3, pp. 67–89, 2005. (in Japanese).
Sanae Fujita and Francis Bond. A Method of Creating New Valency Entries. MachineTranslation Journal, Vol. 21, No. 1, pp. 1–28, 2007.

International Conferences
Sanae Fujita and Francis Bond. A Method of Adding New Entries to a Valency Dictionary by Exploiting Existing Lexical Resources. In Proceedings of the Ninth International Conference on Theoretical and Methodological Issues in Machine Translation: TMI-2002, pp. 42–52, Keihanna, Japan, 2002.
Sanae Fujita and Francis Bond. Extending the Coverage of a Valency Dictionary. In
Proceedings of COLING-2002 workshop on Machine Translation in Asia, pp. 67–
73, Taipei, 2002.
Francis Bond and Sanae Fujita. Evaluation of a Method of Creating New Valency
Entries. In Proceedings of the 9th Machine Translation Summit: MT Summit IX, pp.
16–23, New Orleans, 2003.
Sanae Fujita and Francis Bond. A Method of Creating New Bilingual Valency Entries
159using Alternations. In Proceedings of COLING-2004 Workshop on Multilingual
Linguistic Resources, pp. 41–48, Geneva, 2004.
Sanae Fujita and Francis Bond. An Automatic Method of Creating New Valency
Entries using Plain Bilingual Dictionaries. In Proceedings of the 10th International Conference on Theoretical and Methodological Issues in Machine Translation: TMI-2004, pp. 55–64, Baltimore, 2004.
Sanae Fujita, Francis Bond, Stephan Oepen, and Takaaki Tanaka. Exploiting Semantic
Information for HPSG Parse Selection. In Proceedings of ACL-2007 Workshop on
Deep Linguistic Processing, pp. 25–32, Prague, Czech Republic, June 2007.

List of Other Publications
Refereed Journal Publications
Francis Bond, Sanae Fujita, and Takaaki Tanaka. The Hinoki Syntactic and Semantic
Treebank of Japanese. Language Resources and Evaluation, Vol. 40, No. 3–4, pp.
253–261, 2006. (Special issue on Asian language technology).

International Conferences
Francis Bond, Eric Nichols, Sanae Fujita, and Takaaki Tanaka. Acquiring an Ontology
for a Fundamental Vocabulary. In Proceedings of the 20th International Conference
on Computational Linguistics: COLING-2004, pp. 1319–1325, Geneva, 2004.
Sanae Fujita and Francis Bond. Evaluation of a Method of Creating New Valency
Entries. AAMT Journal, No. 35, pp. 19–20, 2004. (in Japanese).
Takaaki Tanaka, Francis Bond, Stephan Oepen, and Sanae Fujita. High Precision
Treebanking – Blazing Useful Trees Using POS Information. In Proceedings of the
43nd Annual Meeting of the Association for Computational Linguistics: ACL-2005,
pp. 330–337, 2005.
Takaaki Tanaka, Francis Bond, Stephan Oepen, and Sanae Fujita. High Precision
Treebanking – Blazing Useful Trees Using POS Information. In Proceedings of the160

11th Annual Meeting of The Association for Natural Language Processing: NLP2005, pp. 994–997, Takamatsu, 2005.
Sanae Fujita, Takaaki Tanaka, Francis Bond, and Hiromi Nakaiwa. An Implemented
Description of Japanese: The Lexeed Dictionary and the Hinoki Treebank. In Proceedings of COLING/ACL-2006 Interactive Presentation Sessions, pp. 65–68, Sydney, 2006.
Eric Nichols, Francis Bond, Takaaki Tanaka, Sanae Fujita, and Daniel Flickinger. Robust Ontology Acquisition from Multiple Sources. In Proceedings of COLING-2006
2nd Workshop on Ontology Learning and Population: Bridging the Gap between
Text and Knowledge, pp. 10–17, Sydney, 2006.
Takaaki Tanaka, Francis Bond, and Sanae Fujita. The Hinoki Sensebank — A
Large-Scale Word Sense Tagged Corpus of Japanese —. In Proceedings of the
COLING/ACL-2006 Workshop on Frontiers in Linguistically Annotated Corpora
2006, pp. 62–69, Sydney, 2006.
Takaaki Tanaka, Francis Bond, Timothy Baldwin, Sanae Fujita, and Chikara
Hashimoto. Word Sense Disambiguation Incorporating Lexical and Structural Semantic Information. In Proceedings of the 2007 Joint Conference on Empirical
Methods in Natural Language Processing and Computational Natural Language
Learning: EMNLP-CoNLL-2007, pp. 477–485, 2007.
Hirotoshi Taira, Sanae Fujita, and Masaaki Nagata. A Japanese Predicate Argument
Structure Analysis using Decision Lists. In Proceedings of the 2008 Conference on
Empirical Methods in Natural Language Processing: EMNLP-2008, pp. 522–531,
Honolulu, Hawaii, October 2008. Association for Computational Linguistics.
Timothy Baldwin, Su Nam Kim, Francis Bond, Sanae Fujita, David Martinez, andTakaaki Tanaka. MRD-based Word Sense Disambiguation: Further Extending Lesk.
In Proceedings of the Third International Joint Conference on Natural Language
Processing: IJCNLP-2008, pp. 775–780, Hyderabad, India, 2008.
161

Others
Francis Bond, Timothy Baldwin, and Sanae Fujita. Detecting Alternation Instances ina Valency Dictionary. In Proceedings of the 8th Annual Meeting of The Association
for Natural Language Processing: NLP-2002, pp. 519–522. The Association for
Natural Language Processing, 2002.
Sanae Fujita and Francis Bond. Kōtaikankei-wo riyōshita ketsugōkajisho-no kakutokuhōhō [A Method of Creating a Valency Dictionary using Alternations]. In
Proceedings of the 9th Annual Meeting of The Association for Natural Language
Processing: NLP-2003, pp. 361–364, 2003. (in Japanese).
Francis Bond, Sanae Fujita, Chikara Hashimoto, Kaname Kasahara, Shigeko
Nariyama, Eric Nichols, Akira Ohtani, Takaaki Tanaka, and Shigeaki Amano. The
Hinoki Treebank: A Treebank for Text Understanding. In IEICE Technical Report:
2004-NLC-159, pp. 83–90, 2004. (in Japanese).
Francis Bond, Sanae Fujita, Chikara Hashimoto, Kaname Kasahara, Shigeko
Nariyama, Eric Nichols, Akira Ohtani, Takaaki Tanaka, and Shigeaki Amano. The
Hinoki Treebank: Working toward Text Understanding. In Proceedings of the 5thInternational Workshop on Linguistically Interpreted Corpora: LINC-04, pp. 7–10,
Geneva, 2004.
Francis Bond, Sanae Fujita, Chikara Hashimoto, Kaname Kasahara, Shigeko
Nariyama, Eric Nichols, Akira Ohtani, Takaaki Tanaka, and Shigeaki Amano. The
Hinoki Treebank: A Treebank for Text Understanding. In Proceedings of the FirstInternational Joint Conference on Natural Language Processing: IJCNLP-2004,
pp. 554–559, Hainan Island, 2004.
Francis Bond, Sanae Fujita, Chikara Hashimoto, Shigeko Nariyama, Eric Nichols,
Akira Ohtani, and Takaaki Tanaka. Development of the Hinoki Treebank Basedon a Precise Grammar. In IEICE Technical Report: 2004-NLC-159, pp. 91–98,
2004. (in Japanese).
Sanae Fujita and Francis Bond. Evaluation of a Method of Creating New Valency
Entries. AAMT Journal, No. 35, pp. 19–20, 2004. (in Japanese).
162

Kaname Kasahara, Hiroshi Sato, Francis Bond, Takaaki Tanaka, Sanae Fujita, Tomoko
Kanasugi, and Shigeaki Amano. Construction of a Japanese Semantic Lexicon:
Lexeed. SIG NLC-159, IPSJ, Tokyo, 2004. (in Japanese).
Francis Bond, Sanae Fujita, Chikara Hashimoto, Kaname Kasahara, Shigeko
Nariyama, Eric Nichols, Akira Ohtani, Takaaki Tanaka, and Shigeaki Amano. The
Hinoki Treebank: A Treebank for Text Understanding. Lecture Notes in Computer
Science, Natural Language Processing: IJCNLP 2004, Vol. 3248, pp. 158–167,
2005.
Francis Bond, Sanae Fujita, Takaaki Tanaka, and Hiromi Nakaiwa. Nihongo-notōgo/imi kōpasu Hinoki [Japanese Synactic-Semantic Corpus — Hinoki]. In Proceedings of the 11th Annual Meeting of The Association for Natural Language Processing: NLP-2005, pp. 490–498, Takamatsu, 2005. (in Japanese).
Takaaki Tanaka, Francis Bond, Sanae Fujita, and Chikara Hashimoto. Word Sense
Disambiguation Incorporating Lexical and Structural Semantic Information. In Proceedings of the 13th Annual Meeting of The Association for Natural Language Processing: NLP-2007, pp. 1086–1089, 2007. (in Japanese).
Sanae Fujita, Francis Bond, Stephan Oepen, and Takaaki Tanaka. Parse Ranking with
Lexical and Structural Semantics. In Proceedings of the 13th Annual Meeting of The
Association for Natural Language Processing: NLP-2007, pp. 1090–1093, 2007. (inJapanese).
Sanae Fujita, Francis Bond, and Akinori Fujino. Word Sense Disambiguation using
Disambiguated Superordinate Semantic Classes. In Proceedings of the 14th Annual
Meeting of The Association for Natural Language Processing: NLP-2008, pp. 568–
571, 2008. (in Japanese).

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