Language Resource Addition Strategies for Raw Text Parsing
Atsushi Ushiku 1 , Tetsuro Sasada 2 , Shinsuke Mori 3

Graduate School of Informatics, Kyoto University
Academic Center for Computing and Media Studies, Kyoto University
1,2,3
Yoshidahonmachi, Sakyo-ku, Kyoto, Japan
1

2,3

Abstract
We focus on the improvement of accuracy of raw text parsing, from the viewpoint of language resource addition. In Japanese, the raw text
parsing is divided into three steps: word segmentation, part-of-speech tagging, and dependency parsing. We investigate the contributionof language resource addition in each of three steps to the improvement in accuracy for two domain corpora. The experimental results
show that this improvement depends on the target domain. For example, when we handle well-written texts of limited vocabulary, white
paper, an effective language resource is a word-POS pair sequence corpus for the parsing accuracy. So we conclude that it is importantto check out the characteristics of the target domain and to choose a suitable language resource addition strategy for the parsing accuracy
improvement.
Keywords: language resource addition, part-of-speech tagging, dependency parsing

1.

Introduction

For languages without clear word boundary natural language processing (NLP) , such as word segmentation (WS),part-of-speech (POS) tagging (PT), and dependency parsing (DP) is essential technology for applications such asmachine translation or information extraction (Matsumoto
et al., 2000; Kudo et al., 2004; Kurohashi et al., 1994; Neubig et al., 2011; Kurohashi and Nagao, 1994; Mori et al.,
2000; Kudo and Matsumoto, 2002; Flannery et al., 2012).
WS and PT are now sufficiently accurate and widely usedin various applications. DP is, however, not so widely used
as WS and PT because of its insufficient performance. Recently there are many attempts at using sentence structuresuch as tree-based machine translation, language generation, etc. Thus the accuracy of a dependency parser is getting more and more important.
In this paper, we discuss DP from raw sentences inJapanese. Although Japanese does not have clear wordboundary, we take words as the DP unit similar to NLP
in other languages such as English (Buchholz and Marsi,
2006; McDonald and Pereira, 2006; McDonald et al., 2005;
Nivre and Nilsson, 2005; Koo and Collins, 2010; Oflazer,
1999; McDonald et al., 2006; Sagae and Lavie, 2006; Eisner, 1996; Nivre and Scholz, 2004), and we adopt a threestep pipeline method consisting of a word segmenter, a POS
tagger, and a dependency parser (Figure 1). Although someresearchers have proposed joint approaches (Hatori et al.,2012), the pipeline method has advantages that it can utilize
various types of language resources easily and it is easy toanalyze the effects of each step. So we can say the pipeline
method is more realistic than joint approaches. Actually for
WS and PT available training data are usually larger thanthose for DP especially in domain adaptation situations.
In this paper we assume that we want accurate DP resultsof raw texts in a new target domain and the method is language resource addition to each step because it is the easiest
and sometimes most effective method for improving the accuracy. We propose several variations of language resource
addition strategies and compare them in DP accuracy.

In the experiments, we use core data of Balanced Corpus
of Contemporary Written Japanese (BCCWJ) (Maekawa,
2008) which has six sub-domains (Yahoo!Answers, white
paper, Yahoo!Blog, book, magazine, and newspaper). We
select white paper and Yahoo!Blog as target domains to investigate language resource addition strategies. The reason
is that two domains have the lowest DP accuracies and thecauses are different. At WS and PT steps, the possible language resources are word dictionaries and fully annotatedcorpora. At DP step, the language resource is fully annotated corpora.
In the following sections, we briefly explain WS, PT, DP,and language resources for them. Then we experimentallycompare the performances in accuracy of DP from raw texts
under various settings. Finally we discuss the strategies
from the viewpoints of efficiency and cost.

2.

Word Segmentation

WS is the first step, which takes an unsegmented text as the
input and segments it into a sequence of words. We adopt
one of the state-of-art methods, pointwise method (Neubig
and Mori, 2010; Neubig et al., 2011; Mori and Neubig,2014). This method is capable of utilizing various typesof language resources and thus useful in domain adaptation
situations.

2.1. Method
The pointwise word segmenter takes a sequence of characters x = x1 x2 · · · xn as input. Then it makes a classification at every point between two characters (decision
point). Each classification is binary indicating whether
there is a word boundary (Y) or not (N) as shown in
Figure 2. Normally SVM (Fan et al., 2008) is used forclassification referring to the features described in Table
1. They are derived from surrounding characters x =xi−m+1 xi−m+2 · · · xi+m , where m is the window size.
Assuming that a discriminant function is f (x, bi ), the word
segmenter calculates the following for each i:bˆi = argmax f (x, bi ).
bi ∈{Y,N}664

Raw!text	
I!will!design!a!counter)!

!

Word!sequence
	
 | |
| |
| |
!
I
a!counterdesign will

Word=POS!pair!sequence	/noun| /par9cle|I/noun| /par9cle|
a!counter/noun| /verb|
design/aux.!verb!!
will/noun| /par9cle|I!
!
/noun| /par9cle|
a!counter/noun| /verb|
design/aux.!verb!!
willOutput	

Fig. 1: Pipeline combination of word segmentation, POS tagging, and dependency parsing.
★✥ ★✥
★✥
★✥ ★✥
ti ★✥
xi−m+1

···

xi−1

xi

xi+1

xi+2

···

xi+m

✧✦ ✧✦
✧✦
✧✦
✧✦ ✧✦
Fig. 2: The characters to be referred to for the decision point ti .
From the sequence of decisions b = b1 b2 · · · bn−1 , we obtain a word sequence for the input.

2.2. Language Resources for Word
Segmentation
The parameters of f (x, bi ) are estimated from annotatedcorpora and/or dictionaries (lists of spellings). Below we
explain these language resources, with an example sentence“自分でカウンタを設計しよう,” where “カウンタ” is a
new word.
Word dictionary: We add “カウンタ” to a word dictionary.
Fully annotated corpus: The traditional training data haslabels at all decision points as follows.
自分|で|カウンタ|を|設計|し|よう3.

POS Tagging

The second step is PT, which takes a sequence of words
as input and estimates a POS tag for each word. For an
automatic POS tagger, we adopt pointwise method (Neubig
et al., 2011) because of its flexibility of language resource
addition.

3.1. Method
The pointwise POS tagger takes a target word wi with theleft character sequence x− and the right character sequencex+ for the context as shown in Figure 3. Then it estimates
the POS tag ti of wi by referring to the context. Note thatit does not refer to the estimated POS tags of other words
nor word boundary information other than wi to allow flexible language resource addition. Each estimation is multiclass classification and Neubig et al. (2011) reported that
SVM (Fan et al., 2008) achieves comparable accuracy to

665

W1:

character n-gram

W2:character type n-gram

W3:

word dictionary

Table 1: The features for word segmentation.
We use a sequence of characters around a decision point, whose window size is m
and length is n.
We translate characters into character types (kanji, katakana, hiragana, Roman
letters, numbers, and other), and the feature is a sequence of character types assame as character n-gram.
We use following flags at the decision point ti for the length k.
(1) whether the left character sequence x1−k+1 x1−k+2 · · · xi is in a dictionary.
(2) whether the right character sequence xi+1 x1+2 · · · xi+k is in a dictionary.
(3) whether the crossing character sequence xi−j+1 xi−j+2 · · · xi−j+k (1 ≤ ∀j <k) is in a dictionary.

★✥ ★✥
★✥
★
x−n′

···

x−2

x−1

wi

✧✦ ✧✦
✧✦
✧

✥
★✥
★✥ ★✥
x+1

x+2

···

x+n′

✦
✧✦
✧✦ ✧✦

Fig. 3: The characters to be referred to for the word wi .
Table 2: The features for POS tagging.
P1:
P2:

word wicharacter n-grams in x− x+P3:character type n-grams in x− x+

A partial sequence of characters whose window size is n′ and length is1, 2, · · · , n around the decision point.
We translate characters into character types. The feature is a part of sequence of character types (same as character n-gram in Table 1).

sequence-based method.
Assuming that a discriminant function is f (w, ti ), the POS
tagger calculates the following for each i:tˆi = argmax f (w, ti ),ti ∈Twhere T = {noun, verb, particle,aux.verb, ...}. We also use
SVM referring to the features described in Table 2.

3.2. Language Resources for POS Tagging
Similar to WS, the pointwise POS tagger is capable of utilizing the following two types of language resources. Hereagain we use the same example as in WS with a new word
“カウンタ/noun.”
Word-POS pair dictionary: We add pairs of a word and
its POS tag (e.g. “カウンタ/noun”) to the dictionary.
Fully annotated corpus: The traditional training data for
POS taggers contains a sequence of word-POS pairs.
An example is as follows:
自分/noun|で/particle|
カウンタ/noun|を/particle|
設計/noun|し/verb|よう/aux.verb.

4. Dependency Parsing
DP takes a sequence of words and decides dependency relations among them. Among various solutions, that based on
the maximum spanning tree (MST) framework (McDonald and Pereira, 2006; McDonald et al., 2005) is one of
the state-of-the-art methods. We adopt an MST variation(Flannery et al., 2012) which is capable of utilizing various
types of language resources. Below, we overview the MST
dependency parser and explain language resources for it.

4.1. Method
A Dependency parser takes a sequence of words w =w1 , w2 , . . . , wn and a sequence of POS tags t =t1 , t2 , . . . , tn as input and estimates the dependency treed = ⟨d1 , d2 , . . . , dn ⟩, where di = j indicates that thehead of wi is wj .
The MST parser firstly assigns edge scores σ(i, j, w) =exp (θ ·φ(i,j,w ))!
, where φ(i, j, w) is a real value vecj exp (θ ·φ(i,j,w ))tor calculated from the features shown in Table 3 and θ isa vector of weights for features estimated from a training
data. Then the parser searches for the MST as follows:n!
d̂ = argmax
σ(i, j, w).
d∈D i=1

666

D1:
D2:
D3:
D4:
D5:
D6:
D7:

Usage
training

test

Table 3: The features for dependency parsing.
The distance between a dependent word wi and its candidate head wj .
The surface forms of wi and wj .
The POSs of wi and wj .
The surface forms of up to three words to the left of wi and wj .
The surface forms of up to three words to the right of wi and wj .
The POSs of the words selected for D4.
The POSs of the words selected for D5.

DomainYahoo!Answers
white paper
Yahoo!Blog
bookmagazine
newspaper
Yahoo!Answers
white paper
Yahoo!Blog
bookmagazine
newspaper

Table 4: Corpus specifications.
(symbol) #Sentences #Words #Characters
(OC)
1,614
33,07846,435(OW)
1,552
62,73590,610(OY)
1,858
31,56346,481(PB)
2,254
53,03773,194(PM)
2,514
42,80065,245(PN)
2,590
57,31983,985
(OC)
500
9,846
29,038(OW)
504
23,952
71,352
(OY)
509
9,239
27,208(PB)
511
11,792
34,865
(PM)
495
7,415
21,750(PN)
505
12,62137,3584.2. Language Resource for Dependency Parsing
For the dependency parser, the language resource is a fully
annotated corpus.
Fully Annotated Corpus: Traditionally a training sentence for dependency parsers contains the dependency
relationships among all its words. Thus it implies thatthe sentence is divided into words but it does not need
POSs for the words.

5. Strategy Comparison
In order to obtain knowledge of language resource addition
strategy, we measured the contributions to the parsing accuracy of language resource additions to the sub-steps. In thissection, we first describe the settings of experiments, thenevaluate their results, and finally discuss the strategies.

5.1. Language Resources
For the experiments, we used a part of BCCWJ (Maekawa,
2008) annotated with dependency trees provided by (Moriet al., 2014). Thus the sentences are divided into words annotated with POS tags and tree structures. BCCWJ has six
sub-domains (Yahoo!Answers, white paper, Yahoo!Blog,
book, magazine, and newspaper). We divided each of theminto a training and test part (Table 4). We also use a dictionary, UniDic (Den et al., 2007), containing 234,653 wordPOS pairs. We separated inflectional endings from thestems.

5.2. Evaluation Criteria
The PT evaluation criterion is the same as morphologicalanalysis F-measure (Nagata, 1994). Let MREF be the num-

#Words/#Sentences
20.49
40.42
16.99
23.53
17.02
22.13
19.69
47.52
18.15
23.08
14.98
24.99

ber of word-POS pairs in the correct sentences, MSY S be
the number of word-POS pairs in the output sentences, andMCOR be the number of the word-POS pairs in both of thecorrect sentences and the output sentences, then the precision P and the recall R are defined as follows:
P = MCOR /MREF ,R = MCOR /MSY S .
The total evaluation criterion is F-measure, the harmonicmean of the precision and the recall. For WS evaluation,
we only compare words ignoring POSs.
For DP evaluation we count the number of dependency relations whose sources and destinations meet those of thecorrect relations. A dependency relation has a source wordPOS pairs and a destination word-POS pairs. Let DREF
be the number of dependency relations in the correct sentences, DSY S be the number of dependency relations in
the output sentences, and DCOR be the number of the dependency relations in both of the correct sentences and theoutput sentences, then the precision P and the recall R aredefined as follows:
P = DCOR /DREF ,R = DCOR /DSY S .
The total evaluation criterion is F-measure, the harmonicmean of precision and recall. Because Japanese dependencies are always from left to right and the last word does nothave a destination, we exclude it.

667

Table 5: The accuracies and coverages of each domain.
Domain(symbol)Yahoo!Answers (OC)white paper(OW)
Yahoo!Blog
(OY)
book
(PB)
magazine(PM)newspaper(PN)

Word segmentation
(WS)
97.88
98.78
96.52
98.05
97.41
97.64

POS tagging
(PT)
95.93
96.67
93.90
95.75
94.53
95.67

Dependency parsing
(DP)
87.09
82.93
83.03
84.13
83.75
83.52

Coverage(word)
99.87
99.97
98.06
99.95
99.72
99.37

Coverage(word-POS pair)
99.82
99.91
97.82
99.94
99.57
98.94

Table 6: The accuracies and coverages of white paper (OW).
Language resource
addition strategy--/--/-Wd/--/-Wf/--/-Wd/Pd/-Wf/Pf/-Wf/--/Df
Wf/Pf/Df

Word segmentation
(WS)
98.78
98.78
99.09
98.78
99.09
99.09
99.09

POS tagging
(PT)
96.67
96.67
96.94
96.67
98.25
96.94
98.25

Dependency parsing
(DP)
82.93
82.93
83.30
82.93
85.16
84.44
86.53

Coverage(word)
99.97
99.98
99.98
99.98
99.98
99.98
99.98

Coverage(word-POS pair)
99.91
99.91
99.91
99.92
99.94
99.91
99.94

Table 7: The accuracies and coverages of Yahoo!Blog (OY).
Language resource
addition strategy--/--/-Wd/--/-Wf/--/-Wd/Pd/-Wf/Pf/-Wf/--/Df
Wf/Pf/Df

Word segmentation
(WS)
96.52
96.64
97.22
96.64
97.22
97.22
97.22

POS tagging
(PT)
93.90
94.01
94.46
93.99
94.99
94.46
94.99

We also calculated the coverage of words or word-POS
pairs. They are defined as the percentages of the units (tokens) in the test set appearing in the training set or the dictionary.

5.3. Strategies
In this section, we explain language resource addition
strategies. We assume frequent adaptation situations where
we want accurate dependency parsing results of the texts in
a new domain. For example, when we think of new NLP
applications, the texts may differ from the general domain
texts and we encounter the problem that the accuracy of thedefault model is very low. Thus in the experimental settings
the baseline is the model trained only from a general corpus and dictionary, denoted by --/--/-- meaning that
we use no additional language resources for WS, PT, andDP. The general domain corpus is the concatenation of the
sub-domains other than the target domain.
As the language resource addition strategies we add some
types of language resources to the baseline as follows tosimulate the above-mentioned situation.

Dependency parsing
(DP)
83.03
83.16
83.64
83.13
84.48
84.41
85.12

Coverage(word)
98.06
98.93
98.93
98.93
98.93
98.93
98.93

Coverage(word-POS pair)
97.82
97.82
97.82
98.68
98.77
97.82
98.77

only in the training data in the target domain to thedictionary.
3. Wf/--/-- : Full annotation for WS. We add the training corpus in the target domain discarding the POS
and dependency information. Thus the corpus contains word sequences.
4. Wd/Pd/-- : Word-POS pair dictionary. We add thewords in Wd/--/-- with the POS tag of the first appearance in the target training corpus. We do not add
the word with other POSs. The reason is that it is notrealistic because we need to check all the appearancesof the word in the (training) corpus to do so. Note that
this is the superset of Wd/--/-- .
5. Wf/Pf/-- : Full annotation for PT. We add the training corpus in the target domain discarding the dependency information. Thus the corpus contains wordPOS pair sequences. Note that this is the superset ofWf/--/-- .

1. --/--/-- : The baseline.

6. Wf/--/Df : We add the training corpus discarding
POS information.

2. Wd/--/-- : Word dictionary. We add words appearing

7. Wf/Pf/Df : We add the training corpus.

668

87#

Wf/Pf/Df#
86.53#

86#

Wf/Pf/!!#
85.16#

F!measure85#

Wf/Pf/Df#85.12#
Wf/Pf/!!#
84.48# Wf/!!/Df#
84.41#

Wf/!!/Df#84.44#

Wf/!!/!!##
83.64#

84#

83#

Wf/!!/!!#
83.30#!!/!!/!!##
82.93#!!/!!/!!###
83.03#

82#

81#

OY

OW

Fig. 4: The accuracies of each domain and strategy.

With these models, we can figure out which annotation iseffective for raw text parsing.

5.4. Test Domains
To select the test domain, we conducted parsing experiments without using in-domain data. Table 5 shows theresults. From this table, we see that white paper (OW) andYahoo!Blog (OY) have the lowest DP accuracies. Interestingly the causes are different. For OY the accuracy of the
WS step is low and it seems to lower the DP accuracy. On
the other hand, the WS and PT accuracies of OW are thehighest among six, but the DP accuracy is the lowest. The
reason may be that the sentences in OW are written by theprofessional and very formal, while the sentences in OY are
so-called user generated contents and contain many unusual
words such as emoticons. The coverages (low for OY andhigh for OW) shown in Table 5 also support this reasoning.
In addition, the sentences of OW tend to be longer thanthose of others (Table 4), making DP of OW much moredifficult. From these observations we selected OW and OY
as the target domains to investigate language resource addition strategies.

5.5. Results and Discussion
We conducted the pipeline combination of WS, PT, and DPof the strategies described above and measured the performances at each step. Table 6 and Table 7 show the results.
Note that some values are theoretically equal (e.g. the WS
accuracies of Wf/--/-- , Wf/Pf/-- , Wf/--/Df , and
Wf/Pf/Df ).
From the results we see that language resource additions

improve DP accuracies in the both domains. And an annotated corpus addition is always better than a dictionary
addition.
To compare the contributions of the annotated corpora to
the DP accuracies, we show graphs in Figure 4.
The effectiveness of language resource addition to each stepdiffers greatly depending on the domain. For example, theeffectiveness of WS for OW is much lower than that for OY
as shown in Figure 4. This is because WS of OY is difficult,and language resources improve the coverage of OY.
Therefore, for OW it is not a good strategy to annotate atarget corpus with word segmentation only (Wf/--/-- ).
Instead it is a better strategy to add word-POS pairs(Wf/Pf/-- ). The accuracy of Wf/Pf/-- in OW(85.16) is higher than that of Wf/--/Df in OW (84.44).
In addition, dependency annotation is more costly than POS
annotation. Thus the POS annotation is much more effective than dependency annotation of OW.
On the other hand, for OY preparing word segmented sentences (Wf/--/-- ) is not a bad annotation strategy. Because word boundary annotation is the less costly, this strategy improves most effectively when we can spare only
small cost for adaptation. The accuracy of Wf/Pf/-(84.48) is slightly higher than that of Wf/--/Df (84.41)
for OY, we can say that POS annotation is more effective
considering the annotation costs.
In summary, the effectiveness of language resource addition
to each step depends on the domain, or the characteristicsof the domain. For well-written texts of limited vocabulary like OW, an effective language resource is a word-POS

669

pair sequence corpus. Contrary for sentences in user generated contents like OY, adding a corpus containing word
sequences or word-POS pair sequences is a good strategy
depending on how much you want to improve the accuracy. We may guess which type a new target domain falls
into by checking the coverage of the target domain on asmall portion of sentences segmented into words. Whenthe target domain has low coverage like user-created content, the strategy in OY is suitable. When we are targeting
texts whose coverage is very high, the strategy of OW allows us to improve the accuracy more effectively.

6. Conclusion
In this paper, we surveyed how to improve the accuracy of
DP by adding language resources on the premise of pipeline
method.
The language resources are word boundary information,
POS tags, and dependency relations. We compared various methods for adding language resources. The experimental results indicated that the effectiveness of language
resource addition to each step is different depending on thetarget domain. For example, when we handle well-written
texts of limited vocabulary, an effective language resource
is a word-POS pair sequence corpus. Thus it is importantto check out the characteristics of the target domain and select a language resource addition strategy for an efficientDP accuracy improvement.

7. Acknowledgments
This work was supported by JSPS Grants-in-Aid for Scientific Research Grant Number 26280084 and NTT agreement dated 06/08/2015.

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