Examining Crosslingual
Word Sense Disambiguation
Liling Tan

School of Humanities and Social Sciences
A thesis submitted to the Nanyang Technological University
in partial fulfillment of the requirement for the degree ofMasters of Arts2013

Acknowledgements
This thesis is made possible by the mentoring and wise advice of Dr. Francis Bond and the authors of theresources that were used to examine Crosslingual Word Sense Disambiguation (CLWSD). The author ofthis thesis thanks also the lecturers and colleagues from the Universität des Saarland for their impartationof knowledge during the 10 months Erasmus exchange and occasional advice during the thesis process.
Finally the author’s gratitude extends to Erasmus Mundus MULTI committee which had made theexchange possible.

Abbreviations
ConceptID (Concept IDentification number) refers to the unique 8 digit identification number followed by a dashand its Part-of-Speech (POS tag). E.g. 07470671-n refers to the synset defined as “a formal contest in which two or
more persons or teams compete”; aka as SenseID (Sense IDentification number).
CLWSD (Crosslingual Word Sense Disambiguation) is the computational task of correctly identifying the foreign
language translations of a polysemous word given an English context sentence
LDA (Latent Dirichlet Allocation) is a generative model that allows sets of observations to be explained byunobserved groups that explain why some parts of the data are similar.
LSA (Latent Semantic Analysis, aka. Latent Semantic Indexing) is a technique in natural language processing, inparticular in vectorial semantics, of analyzing relationships between a set of documents and the terms they containby producing a set of concepts related to the documents and terms.
NLP (Natural Language Processing) is the multidisciplinary field between computer science, artificial intelligence,cognitive science and linguistics concerned with the computational understanding and production of human (natural)
language.
NMF (Non-negative matrix factorization) is a group of algorithms in multivariate analysis and linear algebra wherea matrix, V, is factorized into two matrices W and H.
NTU-MC (Nanyang Technological University - Multilingual Corpus) is a multilingual corpus built by thecomputational linguistic group in NTU
OMW (Open Multilingual Wordnet) is a collection of open source wordnets in a variety of languages, all linked to
the Princeton Wordnet of English (see http://www.casta-net.jp/~kuribayashi/multi/)
PMI (Pointwise Mutual Information) is a statistical associative measure of how much a word tells us about anotherword
SemEval (Semantic Evaluation) is an ongoing series of evaluation workshops on computational semantic analysis
systems (see http://en.wikipedia.org/wiki/SemEval)
Tf-idf (Term frequency-inverse document frequency) is a matrix statistic that reflects the importance of a word to a
document in a corpus.
Topical CLWSD (Topic-model based CLWSD) is the task of using topic-models to resolve lexical disambiguity
WN (WordNet) is a lexical database of words and their respective psycholinguistically motivated concepts
WSD (Word Sense Disambiguation) is the computational task of correctly identifying the sense of a polysemous
word given a context sentence
XLING (CrossLINGual) is the software created for thesis to attempt the Topical CLWSD task
XLING_SnT (CrossLINGual Similar and Translate) is the baseline model of the XLING software described inchapter 6
XLING_TnT (CrossLINGual Topicalize and Translate) is the main model of the XLING software described inchapter 6

Summary
Understanding human language computationally remains a challenge at different levels, phonologically,
syntactically and semantically. This thesis attempts to understand human language's ambiguity throughthe Word Sense Disambiguation (WSD) task. Word Sense Disambiguation (WSD) is the task ofdetermining the correct sense of a word given a context sentence and topic models are statistical models
of human language that can discover abstract topics given a collection of documents.
This thesis examines the WSD task in a crosslingual manner with the usage of topic models and parallel
corpus. The thesis defines a topical crosslingual WSD (Topical CLWSD) task as two subtasks (i) Matchand Translate: finding a match of the query sentence in a parallel corpus using topic models thatprovides the appropriate translation of the target polysemous word (ii) Map: mapping the word-translation
pair to disambiguate the concept respectively of the Open Multilingual WordNet. The XLING WSD
system has been built to attempt the topical WSD task. Although the XLING system underperforms in the
topical WSD task, it serves as a pilot approach to crosslingual WSD in a knowledge-lean manner.
Other than the WSD task, the thesis briefly presents updates on the ongoing work to compile multilingual
data for the Nanyang Technological University-Multilingual Corpus (NTU-MC). Both the NTU-MC
project and the XLING system are related in their attempts to build crosslingual language technologies.
The rest of the thesis will be as follows: Chapter 1 provides an introduction and motivation to the Topical WSD task.
 Chapter 2 briefly surveys the different representations of meaning and concludes with the thesis'take on meaning Chapter 3 provides an overview of different WSD evaluation methods and approaches andpresents the Topical match, translate and map approach to WSD
 Chapter 4 reviews the available knowledge sources for WSD and highlights the resources used
for the Topical WSD task
 Chapter 5 summaries topic modeling and its relation to Natural Language Processing as well as
its usage in the Topical WSD task
 Chapter 6 describes, evaluates and discusses the Topical CLWSD task
 Chapter 7 concludes the main thesis and discusses future work.
 Chapter 8 presents updates on the ongoing NTU-MC project, a parallel corpora project that may
be used for future work on CLWSD.

Examining Crosslingual Word Sense
Disambiguation
Liling Tan

Contents
1 Introduction
1.1 The Power of Parallel . . . . . . . . . . . . . . . . . . . . . .
1.2 Topical CLWSD . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Thesis Walkthrough . . . . . . . . . . . . . . . . . . . . . . .

3
3
5
6

2 The Meanings of Meaning
2.1 Different Meanings of Meaning . . . . . . . . . . . . . . . . .
2.2 Different Representations of Meaning . . . . . . . . . . . . . .
2.2.1 Semantics from Networks . . . . . . . . . . . . . . . .
2.2.2 Semantics as Vectors . . . . . . . . . . . . . . . . . . .
2.2.3 Semantics from Latent Dimensions . . . . . . . . . . .
2.3 Thesis Take on Meaning . . . . . . . . . . . . . . . . . . . . .

7
8
9
10
12
13
16

3 Word Sense Disambiguation
17
3.1 Different WSD Evaluation Methods . . . . . . . . . . . . . . 18
3.1.1 Multilingual vs Crosslingual WSD . . . . . . . . . . . 19
3.2 Approaches to Word Sense Disambiguation . . . . . . . . . . 21
3.2.1 Knowledge-rich systems . . . . . . . . . . . . . . . . . 22
3.2.2 Knowledge-added systems . . . . . . . . . . . . . . . . 22
3.2.3 Knowledge-lean systems . . . . . . . . . . . . . . . . . 23
3.3 Thesis Approach to WSD . . . . . . . . . . . . . . . . . . . . 24
4 Knowledge Sources for WSD
25
4.1 Unstructured resources . . . . . . . . . . . . . . . . . . . . . . 25
4.1.1 Corpora . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.1.2 Collocation Resources . . . . . . . . . . . . . . . . . . 27
4.1.3 Stoplists . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 Structured resources . . . . . . . . . . . . . . . . . . . . . . . 28
4.2.1 Machine-Readable Dictionaries and Thesauri . . . . . 28
4.2.2 Ontologies and Knowledge Bases . . . . . . . . . . . . 29
4.2.3 WordNets . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.3 Resources used for Topical CLWSD . . . . . . . . . . . . . . . 30

1

CONTENTS
5 Topic Models and NLP
5.1 Topic Models with Latent Dirichlet Allocation

2. . . . . . . .

31
33

6 Topical CLWSD
35
6.1 Background and Hypothesis . . . . . . . . . . . . . . . . . . . 36
6.2 System Description . . . . . . . . . . . . . . . . . . . . . . . . 36
6.2.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . 36
6.2.2 Topicalize . . . . . . . . . . . . . . . . . . . . . . . . . 39
6.2.3 Rank . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6.2.4 Translate . . . . . . . . . . . . . . . . . . . . . . . . . 41
6.2.5 Fallback . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6.2.6 Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6.3 CLWSD Task Evaluation . . . . . . . . . . . . . . . . . . . . 42
6.4 CLWSD Results . . . . . . . . . . . . . . . . . . . . . . . . . 43
6.5 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 43
6.6 From CLWSD to Classic WSD . . . . . . . . . . . . . . . . . 47
6.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
6.8 Conclusion for Topical CLWSD . . . . . . . . . . . . . . . . . 51
7 NTU-MC
52
7.1 Linguistically Diverse Corpus . . . . . . . . . . . . . . . . . . 52
7.1.1 Historical log . . . . . . . . . . . . . . . . . . . . . . . 55
7.2 Asian Language NLP tools . . . . . . . . . . . . . . . . . . . 56
7.2.1 Indonesian POS tagger . . . . . . . . . . . . . . . . . . 56
7.2.2 Mini-segmenter . . . . . . . . . . . . . . . . . . . . . . 56
7.2.3 GaChalign . . . . . . . . . . . . . . . . . . . . . . . . 57
8 Conclusion and Future Work58

Appendix A Detailed CLWSD Evaluations

60

Appendix B Gotcha Code Snippets
68
B.1 UTF-8 Processing in Python . . . . . . . . . . . . . . . . . . 69
B.2 Normalizing Accented Latin Characters . . . . . . . . . . . . 70

Chapter 1

Introduction
Language is ambiguous by nature. Consider the word match in the following
sentences:(a) Mustafa may not be as fancy as some of Singapore’s other malls, but
it has a great range of items, and good prices to match.
(b) So if you hanker for watching some high octane local blade action, callthe NIHL at (65) 6276 0364 for more match schedules and updates.
The occurrences of the word match in the two sentences clearly denote
different meanings; respectively, they mean :1(i) be compatible, similar or consistent(ii) a formal contest in which two or more persons or teams compete
Resolving such lexical ambiguities computationally is an Artificial Intelligencecomplete (AI-complete) problem for machines (Mallery, 1988). The computational linguistic task to identify the correct meaning of words in the given
context is called Word Sense Disambiguation (WSD).

1.1

The Power of Parallel

Consider a multilingual approach to WSD where translations of a polysemous word provide complementary information on a different range of meanings (i.e. every translation of a polysemous word encodes a different set of1

The meanings are taken from the Princeton English WordNet (Fellbaum, 1998).

3

CHAPTER 1. INTRODUCTION

4pycholinguistic concepts). Different translations provide complementary information that is synergistic in reducing word ambiguity. For example thesentence (b) is translated as such in Chinese and Japanese:

Given the respective sense inventory in English, Chinese and Japanese,2
we consider all the possible senses of the word match and its translations.
We can easily disambiguate the sense in sentence (b) as 07470671-n 3 : “aformal contest in which two or more persons or teams compete”.
eng: {00041188-n, 00456199-n, 00457382-n, 00557588-n, 07456188-n, 07458453n, 07464725-n, 07468116-n, 07470671-n, 07472327-n, 07472657-n}
mcn: {03728437-n, 03728811-n, 03728982-n, 05696020-n, 07470671-n, 07988857n, 09626238-n, 09900981-n, 13596673-n}
jpn: {00446493-n, 00456199-n, 01168961-n, 07456188-n, 07470671-n, 13596235n}
Navigli & Ponzetto (2012a) validated the effective use of multilingual information on disambiguating word senses using an ontological Knowledge Base
(KB) through graph-based WSD methods. Their experiments were carriedout in 6 closely related European languages. They achieved 6% improvement on SemEval-2010’s all-words WSD evaluation and an improvement of
2% in evaluating SemEval-2010’s lexical substitution task. They concluded
with the paper titled “Joining Forces pays off ”.
This thesis’ direction is in the same exploration of crosslingual information sense disambiguation but in a knowledge-leaner approach (using topic
2

i.e. Princeton WordNet, Chinese WordNet (Xu et al., 2008) and Japanese WordNet(Isahara et al., 2008)
3
In the WordNet, each sense/concept is given an 8 digit identification number, conceptID/senseID followed by a dash and its Part-of-Speech (POS) tag, e.g. -n for noun

CHAPTER 1. INTRODUCTION

5models). WSD tasks are normally defined in a monolingual context, where
systems provide the appropriate sense of a polysemous word given an English sentence. Hence we explore the possibility of finding the closest matchof the context English sentence with sentences from a parallel corpora andconsequently finding the correct sense through the polysemous word and its
translations. We call the task Topical Crosslingual Word Sense Disambiguation (Topical CLWSD).

1.2

Topical CLWSD

Previously, researchers attempted WSD through topic models by using topic
models as global context features in supervised WSD tasks (e.g. Cai et al.
(2007) ; Boyd-Graber et al. (2007)) and by incorporating topics as weights
into probablistic WSD models (e.g. Li et al. (2010)). We propose an alternative sense disambiguation method using parallel corpus as a medium
of external knowledge to reduce the sense ambiguity of polysemous word.
We define the task of Topical Crosslingual Word Sense Disambiguation with
two subtasks:(1) Given a context sentence, find an equivalent sentence and its translation from a parallel corpus(2) Using the word-alignment of the polysemous word from the matchedsentence, generate the ambiguity reduced conceptID(s)For example, given the query sentence, Q, :Q: “In contrast, the rambling valedictory press conference last fall of Notre
Dame’s football coach, Lou Holtz, was criticized by one sportswriterfor its ‘absence of any real sense of closure’.”We use Latent Dirichlet Allocation (LDA) to find the top ranking topic andfrom a parallel corpus and we find a matching English sentence that shares
the same top ranking topic, M with its Spanish translation, T, e.g.:
M : “He failed as a coach for the reason that other great players have failed
as coaches: he thought about himself too much.”
T : “Fracasó como entrenador por la razón de que otros grandes jugadores
han fallado como entrenadores.”CHAPTER 1. INTRODUCTION

6

By taking the word-alignment of coach from M and T, we check the wordpair (coach:entrenador ) in the Open Multilingual Wordnet (Bond andPease, 2013) and the WSD system responds with the appropriate sense(s)that have coach in the English wordnet and entrenador in the Spanish
wordnet; in this case the coach:entrenador word pair yields the conceptID
09931640-n “someone in charge of training an athlete or a team”.
To evaluate the first subtask of finding the correct translation for a polysemous word in the query sentence, we attempt the Crosslingual Word Sense
Disambiguation (CLWSD) task in SemEval-2013. The aim of CLWSD task
in SemEval-2013 is to evaluate systems that provide the appropriate translation(s) for a polysemous word given a context sentence. Thereafter, we
map the gold answers and system answers to the Open Multilingual WordNet (OMW) and check the accuracy of our system in providing the correct
conceptID using traditional precision, recall, F-score measures.

1.3

Thesis WalkthroughThe rest of the thesis is structured as such:
Chapter 2 briefly defines various notions of meaning from different fields,
describes different approaches to lexical semantics and discusses theusage of latent topics as semantic knowledge for the Topical CLWSD
task.
Chapter 3 surveys the different evaluation methods, different approaches
to the WSD and asserts topic models as the thesis’ approach to crosslingual WSD.
Chapter 4 gives an overview of notable resources for Natural Language
Processing (NLP) and lists the resources used in the Topical CLWSD.
Chapter 5 provides a primer on topic models in NLP.
Chapter 6 describes, evaluates and discusses the Topical CLWSD task.
Chapter 7 concludes the main thesis and discusses future work.
Chapter 8 presents updates on the ongoing NTU-MC project, a parallel
corpora project that may be used for future work on CLWSD.

Chapter 2

The Meanings of Meaning
Meaning is classically defined as having two components (Lyons, 1977):
1. Reference, anything in the referential realm (i.e. anything, real or
imagined, that a person may talk about) denoted by a word or expression, and
2. Sense, the system of paradigmatic and syntagmatic relationships between a lexical unit and other lexical units in a language
The first section of this chapter briefly describes the various notions of
meaning from different fields (section 2.1) and the following section gives
an overview to the different approaches to lexical semantics representation(section 2.2). This chapter concludes with the discussion on representing
semantic knowledge as latent topics and its relevance to the thesis task, viz.
Topical Crosslingual WSD.

7

CHAPTER 2. THE MEANINGS OF MEANING

2.1

8

Different Meanings of Meaning

Formal Semantics
Traditionally, formal semantics understand meaning through the construction of precise mathematical/logical models that define relations between
the linguistic expressions (symbols) and the referential worlds (references;
real or imagined).
Theories of formal semantics are non-psychological (truth-conditional, modeltheoretic, possible worlds, situation, etc.), hence meanings can be out ofmind yet of the world. For example, the word dog can refer to “canis familiaris” (let’s call the lexeme dog 1 ); regardless of the mind’s ability to perceivedog 1 , it exist in the natural world nevertheless. Thus to communicate themeaning or idea of dog 1 , one will use the linguistic symbol, dog.
Cognitive Semantics
Cognitive semantics adds the dimension of mental concepts to the formal
semantics diachotomic meaning (of symbols and references). Theories ofcognitive semantics assume that there are psycholinguistic constructs storedin our long-term memory and the usage of these constructs interacts withthe world to form meaning dynamically (Ungerer & Schmid, 1996).
Often, cognitive semantic studies (e.g. prototypes and categories, figureand grounding, frames and construction, etc.) focus on the mental conceptualization of the world and inter-concept mappings and undermine thelinkage between the concepts and their linguistic symbols.
Lexical Semantics
Lexical semantics provides structures that systematically map linguistic expressions to cognitive concepts. Different from the cognitive semanticists,
lexical semanticists are interested in the most optimal approach towards a
unified label-concept system to the representation of meaning.
Previously we have discussed linguistic expressions/symbols as words,
but the dogmatic term should be label. Other than a flat representation of aword, there are variants of the same word that refers to different concepts
and possibly adhere to the different grammaticality (i.e. with different Parts-

CHAPTER 2. THE MEANINGS OF MEANING

9

Of-Speech). Another term that is frequently used in place of label is token.1
The aim of the Word Sense Disambiguation task is to automatically identify
the different label-concept mappings for polysemous labels.

2.2

Different Representations of Meaning

Textual semantic knowledge can be conceived as knowledge about relations
between a word, its concept(s) and their analogous percepts. Different aspects of these relations have been studied:• Word-Concept(s) relations: Knowledge of the word dog can refer to the concepts dog 1 : ”a member of the genus Canis”; dog 2 :”adull unattractive unpleasant girl or woman”; dog 3 :”a smooth-textured
sausage of minced beef or pork usually smoked”• Concept-Concept(s) relations: Knowledge that dog 1 is a kind ofanimal 1 (hypernym), and poodle 1 (hyponym) is a kind of dog 1 , thetail 1 (holonym) is part of the dog 1 .
• Concept-Percepts relations: Knowledge of how a dog sounds (bark 1 ),how a dog is different from a cat (bark 1 vs meow 1 ), what dogs likes(the skeletal bone 1 ), etc.
• Word-Words relations: the word dog tends to collocate with words
such as wag, pet, bone, cat, chilli, hot, slut, frump, etc.
Different approaches to semantic modeling tend to focus on different aspects of relational knowledge. Computationally, textual semantic models
can either be automatically learnt from natural texts or through manual
annotations subscribing to certain ontological framework. The following
subsections describe the different approaches to lexical semantics.

1
For simplicity of reading, we will refer to labels as labels, words or tokens interchangeably for the rest of this thesis.

CHAPTER 2. THE MEANINGS OF MEANING

2.2.1

10

Semantics from Networks

Since the late 1960s, semantic knowledge is traditionally represented in asemantic network with abstract propositions as its edges and concepts as
nodes. For example, canary is-a bird ; bird has wings; penguin can
swim.

Figure 2.1: Canary Example in a Semantic Net - Graphical representation(left) and in Lisp (right)

Semantics Network from Ontology
Early researches on semantic networks focused on Concept-Concept(s) relations where the distinction between words and concepts is typically collapsed under this approach and the abstract propositions were manuallycoded into a conceptual database (Collins & Quillian, 1969). Rogers & McClelland (2008) was able to automatically learnt these conceptual relations
by looking at word frequencies however the scale of their experiment was
rather small (∼40 nodes: 8 input concepts mapping to 30+ attributional
concepts) and the propositions they attempted was quite rudimentary (3edge types: is, can, has). Mapping large scale abstract conceptual relations
remains unsolved. But in the edge of massive information databases (seesection 4.2.2), extracting knowledge relations mapping has shifted from thetraditional ontological classifications to Knowledge-Base Population tasks(Ji & Grishman, 2011).
Semantic Network from Natural Texts
The other approach of semantic representation focuses more on the associative relations between words in natural texts (Word-Words and WordPercepts relations) and their Word-Concepts mappings. For instance, weCHAPTER 2. THE MEANINGS OF MEANING

11

generally associate the word bird with wings, fly, chirp, nest; but we also
accept affiliating bird with less frequently associated words such as turkey,
thanksgiving, early, bee.
These distributional expectations reflect that the polysemous nature of
the word bird to refer to bird as a taxonomical category, to refer to bird as
food and multiple idiomatic uses of bird. Psycholinguistically, semantic network suggests that semantic representation exists in form of a network andthe retrieval of meaning is made through lexical access using our short-term
memory. Computationally, viewing lexical meanings in form of a network is
not unlike graphical abstract data structure and the retrieval of meaning canbe formulated as finding the concept node with the most optimal traversalof the relation path. The automation of learning Word-Percepts and WordConcepts relations from large-scale linguistic corpora was implemented in
the late 1990s (e.g. Lund & Burgess (1996); Landauer & Dutnais (1997)).
Semantic Network Unified (WordNet and SUMO)
WordNets are large lexicons that encode psycholinguistically motivated concepts. These concepts are represented by sets of cognitive synonyms called
synsets (a word entry in a thesaurus). In addition, each synset also has a
gloss that explains the meaning of the concept in plain english (like a dictionary). The Princeton WordNet (Miller et al. (1990); Fellbaum (1998)) is
the de facto semantic resource for WSD. The latest version of the Princeton
WordNet 3.0 contains 117,000 synsets. Then Niles & Pease (2001a) began
the alignment of WordNet concepts onto Suggested Upper Merged Ontology(SUMO) which unifies the ontological and natural text approaches to semantic representation. WordNet today is the idealized combination of both
approaches where Word-Concepts/Percepts mappings are either manually
input or automatically learnt from a corpus Concept-Concepts mappings
are available with SUMO.
Recently researches have implemented creative ways to create WordNets
in other languages by using translations of manually tagged English Corpora(e.g. Japanese WordNet:

Bond et al., 2008; Diab & Resnik, 2002) orusing ontology to build WordNets in other languages (e.g. EuroWordNet:
Vossen (1998)) or creating new WordNets using monolingual or bilingual
dictionaries (e.g. BalkaNet: Tufiş et al., 2004). Vice versa, the SUMO wasenriched by the integration of concepts from different WordNets, Pease &CHAPTER 2. THE MEANINGS OF MEANING

12

Fellbaum (2010) provided a brief history on the work of WordNet and SUMO
integration.

2.2.2

Semantics as Vectors

Semantic knowledge can be thought as a two dimensional vector space where
each word is represented as a point and semantic association is indicated byword proximity. The vector space model focuses on identifying the WordWord and Word-Percept relations. The primary attraction to semantic vectors is that semantic knowledge can be automatically extracted with a raw
corpus without manual annotations or lexicon building.
Researches adopted vector space model to Natural Language Processing
tasks and achieved impressive results in emulating human language usage.
Rapp (2003) used a vector-based model of word meaning and scored 92.5%
on the Test of English as Foreign Language (TOEFL), where the average
human score was 64.5%. Similarly, vector-based semantics scored 56% on
multiple-choice analogy questions in the SAT college entrance test while
humans score a 57% on average (Turney, 2006).
The vector space model was originally developed for the SMART information retrieval system where a point represents a document (instead of aword) and a query is represented as another point in the same space andrelevance of documents is measured by their similarity (i.e. proximity) tothe query vector (Salton, 1971). Instead of measuring document similarity,
Deerwester et al. (1990) proposed word similarity measurement by representing documents as collections of words and the resultant vector of eachdocument refers to a row vector in the term-document matrix. The generalhypothesis of vector semantics assumes that statistical patterns of humanword usage can be used to disambiguate word meanings (i.e. if two word vectors in any frequency based matrix are similar, they have similar meaning).
For further readings on vector semantics, Turney & Pantel (2010) provided
a survey on vector space models.
Vector-based WSD systems started with statistical tweaking of the vector matrix to achieve the state-of-art through normalization, weighting, discounting and smoothing (e.g. tf-idf: Singhal et al. (1996); PMI: Turney(2001)). Then studies attempted vector composition where the meaning ofa target vector a in the context vector b is a function of the vectors, i.e. c =f(a,b) (Kintsch & Kintsch (2001); McDonald & Brew (2004)). More recently,

CHAPTER 2. THE MEANINGS OF MEANING

13

researches have injected syntactic compositions and additional knowledge(such as sense frequencies and ontological relations) to the vector composition, redefining the general class of vector models as: c = f(a,b,R,K) whereR = syntactic relations, and K = external knowledge (Mitchell & Lapata(2008); Erk & Padó (2008); Thater et al. (2011)).

2.2.3

Semantics from Latent Dimensions

Latent dimensions can be thought of as the subconscious workings of thelexical access hidden in the different usage of different words. Latent Semantic Analysis (LSA) and topic modeling are two prominent vectorial andstatistical approaches to dimensionality reduction that emulates human’s
semantic access.
Landauer & Dutnais (1997) proved that LSA is able to emulate human’s
psycholinguistic behavior and Griffiths et al. (2007) had showed that topic
models are capable of various human linguistic behavior such as inducing aperceptual ontological hierarchy and imitating human’s semantic memory.
Computationally, latent variables/spaces semantics have been successful in
Word Sense Disambiguation tasks (e.g. Katz & Goldsmith-Pinkham (2006);
Li et al. (2010); de Cruys & Apidianaki (2011)).

Figure 2.2: Latent Semantic Analysis (LSA) (top) and Topic Modeling(bottom)

CHAPTER 2. THE MEANINGS OF MEANING

14

Semantics in Latent Space
Latent Semantic Analysis (LSA) extracts spatial representation of words
from a corpus of multiple sentences/documents. By feeding a word-document
co-occurrence matrix to the LSA system, LSA decomposes the matrix into
three smaller matrices, U, D and V (see figure 2.2):• U provides an orthonormal basis for spatial representation of words(i.e. matches each word to a latent dimension)• D weighs the dimensions (i.e. determines how different each dimension
is different from each other)• V provides an orthonormal basis for spatial representation of documents (i.e. matches each document to a latent dimension)
To measure word association, the cosine of the angle between the rows of
U matrix has proven to be effective Landauer & Dutnais (1997). By reducing the dimensionality (i.e. the number of columns in U), statistical noise
will decrease and latent correlations among words surfaces. Dimensionality reduction is achieved by the Singular Vector Decomposition (SVD) the
term-document matrix.
Till et al. (1988) studied the time used to processing word meaning
using a priming study where the participants read sentences that contains
ambiguous words and then they were asked to choose perceptual words
related to the sentence after varying delay times. For example, the sentence”Thinking of the amount of garlic in his dinner, the guest asked for a mint.”
and the lexical choices are {money, candy, breath, coins} .2 They found that
longer delay time primes more accurate choices. Landauer & Dutnais (1997)
suggested that the same priming effect can be explained using LSA, the shortdelay could be represented by taking the cosine of the just the ambiguous
word to the lexical choices and the long delay can be modeled when taking
the cosine of the entire sentence to the lexical choices. Griffiths et al. (2007)
argues that, psychologically, LSA can classify words into clusters and morecritically find words that lie between two clusters and identify words that
can appear in two clusters which are less useful in discriminating polysemouswords.
2

correct choices are underlined

CHAPTER 2. THE MEANINGS OF MEANING

15

Katz & Goldsmith-Pinkham (2006) implemented LSA to WSD by adding
LSA reduced dimensions to K-nearest neighbor cosine similarity classifier
to disambiguate word senses. They found that LSA required more uniquecontextual tokens to better differentiate senses. But their pure LSA classification method did not perform better than Term Frequency - Inverse
Document Frequency (tf-idf) based classifier. Also, they improved the tfidf classifier by merging LSA classification through a voting system used inWicentowski et al. (2004).
de Cruys & Apidianaki (2011) explored the use of Non-Negative Matrix Factorization (NMF) to induce senses from latent factors using thesurface words, ngrams and dependency-based context features. NMF also
preforms dimensionality reduction like LSA but it applies a different factorization technique that uses Kullback-Leibler (KL) divergence instead of the
Euclidean distance as in LSA. Minimizing the KL divergence is more representative of language phenomena than Euclidean distance (ED) because ED
requires a normally distributed matrix but KL divergence allows Zipf (1949)
skewed data. Also mathematically, NMF ensures non-negative probabilities
which can be integrated into other NLP systems more easily; it is done by allowing only additive and non-subtractive relations. By mapping the centroidof the induced senses to a sense inventory, de Cruys & Apidianaki (2011)
achieved better state-of-art results on SemEval-2010’s WSI/WSD evaluation
task. Psycholinguistically, NMF provides the same disambiguating information as LSA where Word-Percept relations are discovered by using latentcorrelations among words from matrix factorization.
Semantics in Latent Topics
Topic Modeling is a statistical approach to semantic representation thatassumes hidden topics are embedded in a corpus. Topic modelling uses
the same word-document co-occurrence matrix as LSA and NMF but themethod to extract latent semantic knowledge is different. Different from
LSA’s vector decomposition and deletion, dimensionality reduction for topic
modeling is achieved from statistical inference.
Topic model assumes the existence of latent topic variables that represent the gists of any set of correlated words. The probability distributionof a word (w ) over each documents (d ) is approximated by the probability distribution of the topics given the documents (aka the gist (g); whereCHAPTER 2. THE MEANINGS OF MEANING

16

each topic is a probability distribution over words and each document is a
probability distribution over topics.
Psycholinguistically in semantic intrusion studies, topic model could emulate the gist-based memory word association studies in the Deese-RoedigerMcDermott (DRM) paradigm (Deese (1959); Roediger & Mcdermott (1995)).
In the DRM paradigm, participants are introduced to a list of percepts {bed,rest, awake, tired, dream, snooze, slumber, snore, nap, yawn} that are associatively related to a word but it is not in the list (i.e. the lure word,
sleep). Then participants are asked to recall all the words and 61% of thesubjects falsely recalled sleep. Using topic model inference, Griffiths et al.
(2007) reproduced the Roediger et al. (2001) DRM recall task and foundthat their topic inferred list of words from a corpus wrongly listed the lure
word as human would. And the rank-order correlation of the lure word was
at 0.437 at 95% confidence interval.3
The ability to emulate human’s perception of sematically associative
words (i.e. psycholinguistically mimetic) and the generative nature of topic
modellings (i.e. computationally minimalistic) makes it an appealing method
to find matching sentences for the Topical CLWSD task.

2.3

Thesis Take on MeaningThis thesis adopts the lexical approach to meaning encapsulated in pyscholinguistically concepts as defined in the WordNet and approaches the topical CLWSD task using the topic model’s representation of meaning hidden
within latent topics.
Although representing semantics with statistical inference may seem
heretic, statistical inference on human behavioral tasks are psychologicallygrounded (Anderson & Schooler, 1991); more specifically topic models haveshown success in capturing Concept-Percepts relations (chapter 3.2.3.2).
Technologically, the probabilistic nature of the model allows it to be extensible with extra semantic knowledge and also easily integrated into other
Natural Language Processing tasks. Using topic models, we craft the Topical
Crosslingual WSD task where we use topic models and parallel dictionary
entries to disambiguate meaning of words given a context sentence.
3
Further discussion on topic modeling and its usage in WSD is found in chapter 3.2.3.2
and chapter 5

Chapter 3

Word Sense Disambiguation
Word Sense Disambiguation (WSD) is the ability to computationally determine the correct sense of a word given a particular context. The main
aim of the Word Sense Disambiguation task is to correctly assign the labels to polysemous words given a context sentence. For example given thesentence ”The dog barks at the cat”, WSD systems should correctly labelbark as bark1 (“Woof !Woof ! ”, the onomatopoeia) as the correct sense in
the sentence not tree bark2 .

Figure 3.1: Polysemous BARKThis chapter of the thesis provides an overview of different evaluation methods (section 3.1) and approaches to the WSD task (section 3.2). And finallyconcludes with the Topic Models usage for WSD and brief concluding words
on how topic models will be used in the Topical Crosslingual WSD task forthis thesis.

17

CHAPTER 3. WORD SENSE DISAMBIGUATION

3.1

18

Different WSD Evaluation Methods

As language technology evolves, the Word Sense Disambiguation (WSD)
task grows in different flavors towards various research directions and formore languages:1• Classic monolingual WSD evaluation task uses WordNet as its
sense inventory and is largely based on supervised/semi-supervisedclassification with the manually sense annotated corpora:– Classic English WSD uses the Princeton WordNet as it sense
inventory and the primary classification input is normally basedon the SemCor corpus.
– Classical WSD for other languages uses their respective WordNet as sense inventories and sense annotated corpora tagged intheir respective languages. Often researchers will also tap on the
SemCor corpus and aligned bitexts with English as its source
language• Multilingual WSD evaluation task focuses on WSD across 2 or morelanguages simultaneously, using their respective WordNets as sense
inventories. It evolved from the Translation WSD evaluation task thattook place in Senseval-2. A popular approach to multilingual WSD is
to carry out monolingual WSD and then map the source language
senses onto the corresponding translation of the target polysemous
word.
• Crosslingual WSD (CLWSD) evaluation task is also focused on
WSD across 2 or more languages simultaneously. Unlike the Multilingual WSD tasks, instead disambiguating senses by providing theconcepts from a predefined sense inventory, systems participating in
the CLWSD task provides the most appropriate translations of thetarget polysemous words in their respective target languages.
• Word Sense Induction and Disambiguation task is a combined
task evaluation where the sense inventory is first induced from a fixed
training set data, consisting of polysemous words and the sentence
1This section of the thesis was made available in Wikipedia http://en.wikipedia.org/
wiki/Word-sense_disambiguation#Task_design_choices before the present submission

CHAPTER 3. WORD SENSE DISAMBIGUATION

19

that they occurred in, then WSD is performed on a different testingdata set.

3.1.1

Multilingual vs Crosslingual WSD

Multilingual and Crosslingual Word Sense Disambiguation (WSD) evaluation tasks focused on WSD across two or more languages simultaneously.
While the Multilingual WSD evaluation task uses a fixed sense inventory(i.e. BabelNet), the sense inventory for the Crosslingual WSD evaluation
task is built up on the basis of parallel corpora, e.g. the Europarl corpus.
Multilingual WSD
The Multilingual WSD task is introduced for the current SemEval-2013
workshop. The task is aimed at evaluating Word Sense Disambiguation
systems in a multilingual scenario using BabelNet as its sense inventory.
Unlike similar tasks, like Crosslingual WSD or the Multilingual Lexical Substitution where no fixed sense inventory is specified, Multilingual WSD uses
the BabelNet as its sense inventory. Prior to the development of BabelNet,a bilingual lexical sample WSD evaluation task was carried out in SemEval2007 on Chinese-English bitexts (Jin et al., 2007).
The multilingual WSD task follows the all-word version of classic WSD,
where participating systems are expected to link all occurrences of nounphrases within arbitrary texts in different languages to their corresponding
Babel synsets (Navigli & Ponzetto, 2012b). The evaluation criterion forthe multilingual WSD task follows the standard precision, recall and F1
measures similar to the evaluation for classic WSD.
BabelNet
BabelNet is a very large multilingual semantic network with millions ofconcepts obtained from:• an integration of WordNet and Wikipedia based on an automatic mapping algorithm and• translations of the concepts (i.e. English Wikipedia pages and WordNet synsets) based on Wikipedia cross-language links and the output
of a machine translation system

CHAPTER 3. WORD SENSE DISAMBIGUATION

20

Figure 3.2: Example of a sense label in BabelNet and CLWSD task
Crosslingual WSD
The Crosslingual WSD (CLWSD) task is introduced in the SemEval-2007
evaluation workshop and re-proposed in SemEval-2010 as well as the current
SemEval-2013 workshop. To facilitate the ease of integrating WSD systems
into other Natural Language Processing (NLP) applications, such as Machine Translation and multilingual Information Retrieval, the crosslingual
WSD evaluation task was introduced a language-independent and knowledgelean approach to WSD.
The task is an unsupervised Word Sense Disambiguation task for English
nouns by means of parallel corpora. It follows the lexical-sample variant of
the Classic WSD task, restricted to 20 polysemous nouns. The evaluation
criterion uses a weighted version of the precision and recall metric inspired by
the English lexical substitution task in SemEval-2010. Participating systems
in this evaluation task are free to use any corpus to build up their sense
inventories (e.g. see figure 3.2).

CHAPTER 3. WORD SENSE DISAMBIGUATION

3.2

21

Approaches to Word Sense Disambiguation

Navigli (2009) visualized WSD approaches as on a bidimensional space,
where the vertical axis represents the ratio of sense-annotated to unlabeleddata needed which determines the degree of machine learning supervision.
The horizontal axis represents the amount of knowledge (e.g. lexical inventory, dictionaries, ontology, domain labels). The general WSD approaches
can be summarized as points on the bidimensional space:

Figure 3.3: Different Approaches to WSD(a) Fully unsupervised, knowledge-lean systems that do not use any amountof knowledge (often not even a sense-inventory and only using sentencealigned parallel corpora)(b) Minimally or semi-supervised systems (e.g. self-training or co-training)that uses little amount of sense-tagged data(c) Fully supervised, knowledgeable systems (machine-learning classifiers)
uses feature based machine methods with as much sense-tagged data
as available

CHAPTER 3. WORD SENSE DISAMBIGUATION

22(d,e) Knowledge-added systems that often exploit unstructured semantic
knowledge (see next chapter) about corpus data such as sense dominance (i.e. sense frequencies), domain-labels, collocation lists, syntactic preferences, etc.
(f,g) Knowledge-rich systems uses structured knowledge sources such as ontological graph searches, gloss overlaps using dictionaries/thesauriThis rest of this section presents several methodologies that describe knowledgerich systems, knowledge-added systems and knowledge-lean systems in Crosslingual WSD (CLWSD) context.

3.2.1

Knowledge-rich systems

Overlapping Senses
Knowledge rich approaches such as the Lesk algorithm (Lesk, 1986), Conceptual Density (Agirre & Rigau, 1996) and Random Walk algorithm (Mihalcea,
2006) are primarily overlap based system comparing the target polysemous
word, its context, the corresponding synsets’ glosses and semantic relations
overlaps within a sense inventory/ontology. They suffer from data sparsity when there are no surface token matches between the queried data andtraining data. Mahapatra et al. (2010) overcame the overlapping sparsity
issue by combining an overlapping measure with WordNet based Ich similarity measure (Leacock and Chodorow, 1998) that accounted for semantic
generalization. Their multi/crosslingual disambiguation was done first by
matching the query sentence to the k-Nearest Neighbor in the training dataranked by their combined scoring function, then looking at the translationof the matched sentence.2

3.2.2

Knowledge-added systems

Most Frequent Sense
Zipf (1949) law established the relationship between the probability of language usage and word frequency (i.e. the frequency of any word is inversely proportional to its rank in the frequency table) and that lexical2

Mahapatra et al. (2010) OWNS system participated only in the French CLWSD task
in SemEval-2010

CHAPTER 3. WORD SENSE DISAMBIGUATION

23

access is based on principle of least effort. As much as the highly favored
Most Frequent Sense (MFS) baseline produces rather high accuracy in WSD
tasks, the MFS phenomenon is psycholinguistically motivated too. Balota& Chumbley (1984) concluded that high frequency words were named moreeasily in lexical decision task.
The variant of the MFS in the Crosslingual WSD task is the Most Frequent Translation (MFT); MFTs are the most frequent lemmatized translation result from the automated word alignment process (GIZA++).
Corpus-based Word Experts
For the CLWSD task in SemEval-2010, van Gompel (2010) UvT-WSD (Universiteit van Tilburg-WSD) system took the Tilburg Memory-Based Learner(TiMBL) classification approach sense disambiguation and selecting thetranslation using K-Nearest Neighbor. His system encoded corpus-based
word experts (Ng & Lee (1996a); Hoste et al. (2002)) in form of global context features for the TiMBL classifier; local features includes words, n-grams,
POS and lemma. All words from The UvT system was the top ranking system for Spanish and Dutch CLWSD (nld: 17.7%; spa: 23.42% precision),even with only the word expert feature it ranked second (nld: 15.93%, spa:
19.92%).

3.2.3

Knowledge-lean systems

Unsupervised Graphical Search
From a graphical approach to WSD, Silberer & Ponzetto (2010) attempted
unsupervised crosslingual WSD using multilingual co-occurrence graphs fromthe adapted PageRank algorithm (Agirre & Soroa, 2009) and disambiguate
the sentences by selecting the highest scoring vector on the computed Minimum Spanning Tree, similar to the Hyperlax algorithm (Véronis, 2004).
Siberer and Ponzetto extended the multilinguality by added translation tokens as new nodes and an additional translation edge type; responses fromtheir system will only be from the nodes with translation edges. Their unsupervised approached is the most robust system in that they were the only
system that attempted CLWSD for all five languages selected for the task
in SemEval-2010. Surprisingly, their approach exceeded Mahapatra et al.’s(2010) knowledge-rich sense overlapping-similarity approach.

CHAPTER 3. WORD SENSE DISAMBIGUATION

24

Topic Models for Word Sense Disambiguation
Topic models have been proposed by recent researchers for the WSD task.
For example, Cai et al. (2007) exploited the corpus topics as global context
features for WSD; Boyd-Graber et al. (2007) integrated McCarthy et al.
(2004) approach for finding predominant word senses into a supervised topic
modeling framework. Boyd-Graber and Blei also integrated their WSD system and it led to modest improvements to state-of-art information retrieval
results. Li et al. (2010) proposed a probabilistic topic model based WSD
method and they achieved state-of-art results on SemEval-2007’s coarsegrain WSD evaluation.3 Li et al.’s probabilistic topic model based WSD
proposed a fully unsupervised WSD by reweighting the sense dominancewith the product of the maximum conditional probability of a sense given
the context and the introduction of the latent topic variable. However, allthese WSD methods were evaluated on monolingual English WSD.

3.3

Thesis Approach to WSD

In this thesis, we explore the usage of topic models in crosslingual WSD task,
we refer to the task as Topical Crosslingual WSD. In brief, the approach is tomatch query sentences to the training sentences and use the word-alignments
as the response to the CLWSD query. The choice to use topic models is
primarily based on its extensibility due to its probabilistic and knowledgelean nature. Chapter 5 presents topic modeling in details and chapter 6
describes the Topical Crosslingual WSD task definition, implementation,
results and conclusion.

3
Li et al. (2010) @ 79.99% for all words; MFS @ 78.99, top performing supervised
system (UoR-SSI) @ 83.21% (Navigli & Velardi, 2005)

Chapter 4

To disambiguate language meaning, human knowledge is emulated in theform of structured and unstructured machine-readable data called knowledge sources. These data are essential to associate conceptual senses totheir word representation. They vary from corpora of texts with or without sense annotations to machine-readable dictionary, thesauri, ontologies,
etc. This chapter provides a brief survey of notable knowledge resourcesof various degrees and concludes with the list of resources that the Topical
CLWSD task uses

4.1

Unstructured resources

4.1.1

CorporaCorpora are collections of texts used to model human language. MonolingualEnglish corpora can be either (i) raw, i.e. unlabeled or (ii) annotated with
Part-Of-Speech (POS) tagged, sense-annotated or other annotations useful
in modeling language. Other than type (i) and (ii), parallel/comparable
corpora have an additional alignment feature that is used to link sentencesfrom the source language to the corresponding target language sentences.
Monolingual raw/pos-tagged corpora• Brown Corpus (Francis & Kucera, 1979), a million word balanced
corpus of American English texts, published in the 1961
• British National Corpus (Burnard, 2007), a 100 million word corpus
of written and spoken (transcribed) samples of British English

25

CHAPTER 4. KNOWLEDGE SOURCES FOR WSD

26• Wall Street Journal (WSJ) corpus (Charniak, 2000), a collection of 30
million words from the WSJ• WaCky (Web as Corpora Kool Yntiative) Corpora are built by crawling websites from the .uk, .de and .it domains. The corpora containmore than a billion words each (Baroni et al., 2009).
• Wikipedia dumps as often used as raw corpora for WSD due to theirsize and availability (e.g. Li et al. (2011))
Monolingual sense-tagged corpora• SemCor (Miller et al., 1994) is the most used sense-tagged corpus, it
is a subset of the Brown Corpus (Francis & Kucera, 1964). It includes
352 texts tagged with 234,000 sense tags.
• Defense Science Organization of Singapore (DSO) corpus which includes 192,800 sense-tagged tokens of 191 words from the Brown and
WSJ corpora (Ng & Lee, 1996b)• Open Mind Word Expert corpus is made up of sense-tagged instancesof 288 nouns collaboratively crowd-tagged by web users (Chklovski &Mihalcea, 2002).
• Senseval and SemEval data sets used in the various WSD evaluation
tasks. For a historical and generic task overview of SemEval workshops, see http://en.wikipedia.org/wiki/SemEval. Also Navigli(2009) provided a detailed survey on the SemEval tasks and their respective competing systems.
Parallel raw/aligned corpora• Europarl Corpus (Koehn, 2005) is a parallel and aligned corpus extracted from the proceedings of the European Parliament in 21 European languages. The size ranges from 1 million to 2.5 million sentences
depending on the language pairs. The sentence alignments were processed using the Gale & Church (1993) algorithm.
• Tatoeba Corpus is a collaborative database of 2.2 million example
sentences in 120 languages geared towards language learners. The

CHAPTER 4. KNOWLEDGE SOURCES FOR WSD

27

example sentences are crowd-translated and moderated by web users(Breen, 2003).
Parallel sense-tagged corpora• MultiSemCor is an English-Italian parallel corpus tagged with senses
from the English and Italian wordnet (Bentivogli et al., 2004)• Japanese SemCor (JSemCor) is a sense-tagged corpus of Japanesetranslated from the English SemCor texts; the Japanese senses areprojected across from English WordNet. The corpus consists of 14,169
sentences with 150,555 content words of which 58,265 are sense tagged(Bond et al., 2012).
• NTU-Multilingual Corpus is a collection of parallel and aligned texts(made up of the Cathedral and the Bazaar corpus, Dancing Man corpus and texts from Singapore Tourism Board (STB) websites) taggedwith English, Mandarin and Japanese WordNet senses. (Chapter 7
gives a brief introduction to the NTU-MC and its relation to this thesis).

4.1.2

Collocation Resources

Collocation resources (such as Word Sketch Engine,1 JustTheWord2 andWeb1T 5-grams (Hawker et al., 2007)) registers sequence of words that cooccur more often than would be expected by chance. For example, strongtea (collocates) vs powerful tea (dispreferred).

4.1.3

Stoplists

Stoplists are lists of undiscriminating non-content words such as a, an, the,he, she, etc. Stopwords are removed in most IR/IE or NLP tasks for two
main reasons; (i) the match between a query and a document should bebased on felicitous terms (information) rather than high frequency noncontent words (noise), (ii) the inverted file (i.e. the mapping from numbers
to words) would be reduced by 30-50% (Manning et al., 2008).
12

http://www.sketchengine.co.uk/
http://www.just-the-word.com/

CHAPTER 4. KNOWLEDGE SOURCES FOR WSD

28Dolamic & Savoy (2010) have shown that the usage of stoplist had significant improvement in search engine’s document retrieval using the traditional Okapi BM25. Using other retrieval models, stoplists were able toreduce search while preserving mean average precision in document retrieval.
• MySQL query parser filters this list of English stopwords when users
use full-text queries3• Lucene siphons lists of stopwords for 33 different languages (ranging
from Arabic, European, Scandinavian to Asian languages) when the
StopWordAnalyzerBase is used in indexing or retrieving documents4• Snowball Tartarus uses stoplists for 21 languages (Romance, Germanicand Scandinavia languages) for their text analyzers5• Rank.nl is a popular Search Engine Optimization company that shares
lists of stopwords for their article and page analyzers in 19 languages6

4.2

Structured resources

4.2.1

Machine-Readable Dictionaries and Thesauri

Machine-readable dictionaries (MRDs) were first made available in the 1980s
and have since become a knowledge source for human-language modeling.
Thesauri provide basic lexicographic relations, like synonymy, antonymy andpossibly other semantic relations such as hypernymy, hyponymy, meronymy,
etc. (Kilgarriff & Yallop, 2000)• Longman Dictionary of Contemporary English (LDOCE) (Proctor,
1978) was the most widely used MRD for WSD before the widespread
adoption of WordNet (Miller et al. (1990); Fellbaum (1998))• Roget’s International Thesaurus (Roget, 1852) classified words basedon its relation to (i) abstract notions, (ii) space, (iii) matter, (iv)intellectual ideas, (v) volition and (vi) socio-emotional intuitions. The
latest edition contains 250,000 entries.
3

http://dev.mysql.com/doc/refman/5.1/en/fulltext-stopwords.html
http://lucene.apache.org/core/4 0 0-BETA/analyzers-common/org/apache/lucene/analysis/util/StopwordAnalyze
5
http://snowball.tartarus.org/
6
http://www.ranks.nl/resources/stopwords.html
4CHAPTER 4. KNOWLEDGE SOURCES FOR WSD

29• Macquarie Thesaurus (Bernard, 1987) has more than 200,000 synonyms based on Australian English including Australian colloquialisms(e.g. emo) ,Multiword Expressions (e.g. nutty as fruitcake) and Aboriginal English (e.g. booliman).

4.2.2

Ontologies and Knowledge Bases

An ontology is a categorized set of concepts based on the relationships between concepts, it can be either domain-specific or generic. A Knowledge
Base (KB) is an information repository that collects and organized referential knowledge about the world. KB extends ontologies’ notion of categorizing concepts to include referential facts about a concept. Ontologies areuseful in WSD to map related concepts. Graphically, entities (i.e. hypernyms of generic types like people, events, organization, etc.) are represented
by nodes (e.g. BobDylan and BlondeOnBlonde), the edges are the relations(e.g. created) between two nodes, a fact consist of two nodes and theirconnecting edge (e.g. BobDylan created BlondOnBlonde)
Hypothetically, KBs can be used to constrict the search space of ultraproductive natural language into a finite set of conceptual units; thus improving robustness of WSD systems.
• Suggested Upper Merged Ontology (SUMO) is a generic ontology withmeta-level concepts that do not belong to a specific domain. It contains
25,000 terms and 80,000 axioms (Niles & Pease, 2001b).
• YAGO is a semantic KB derived from Wikipedia, WordNet and SUMO
contains more than 10 million entities and 120 million facts (Suchanek
et al., 2007).
• Freebase is a user-generated KB consisting of structured data of wellknown people, places and things. Each referential entity is a topic
and they contain properties that has one or more values; and each
topic-value pair constitute a fact in the KB. It can be visualized as a
database made up of Wikipedia infoboxes like properties and values.
Freebase contains over 38 million topics and 1.1 billion facts (Bollacker
et al., 2008).
• BabelNet is a hybrid knowledge based built with YAGO and Freebase
like name entities and also traditional hieratical ontology concepts.

CHAPTER 4. KNOWLEDGE SOURCES FOR WSD

30(see chapter 3.1.1)

4.2.3

WordNets

A WordNet is a computational lexicon of psycholinguistic concepts andtheir respective word representations (synsets). Each concept identified
by the lexicographer is tagged with a unique sense identification number(ConceptID).7 (see section 2.2.1 for a brief history of WordNet). WordNets of different language exists in different sizes and licenses; the freer the
license the more a wordnet is used (Bond & Paik, 2012).
• Princeton WordNet (Miller et al., 1990) is the original English WordNet, the latest version 3.0 contains 155,000 synsets covering 117,000
concepts.
• Open Multilingual WordNet (Bond & Pease, 2013) is a repository of
WordNet with open source licenses from over 26 languages totaling to
100 thousand over concepts and 1.4 million word representations forthese concepts.

4.3

Resources used for Topical CLWSD

The approach of Topical Crosslingual WSD is to use minimal knowledge toprovide the best translation of an English word given a context sentence.
Then we search the Open Multilingual Wordnet for the respective senseID(s) of the polysemous English word and the translation word-pair.
The two main resources for the Topical Crosslingual WSD are (i) a parallel corpus and (ii) the Open Multilingual WordNet. The former is used
to build topic models for the matching subtask and the latter use in theproviding the concept ID for the mapping subtask (see chapter 5.1). Also,a minimalistic stoplists was used to reduce topic induction search space.
The rank.nl (section 4.1.3) analyzers’ stoplists because they were the shortest list as we want to preserve as many context words as possible for sense
disambiguation.

7

ConceptID exists mainly in 2 formats (i) Original Princeton format (e.g.
dog%1:05:00::, the pet) and (ii) Sense Offset, an 8 digit integer with POS tag (e.g.
02084071-n). For simplicity, we choose to use the latter for this thesis.

Chapter 5

Topic Models and Natural
Language Processing
Human’s perception and cognition can be understood through computational models that address particular human capacity such as memory, categorization, problem solving, pattern recognition, etc. (Marr (1982); Anderson & Bower (1974)). Human’s inference and prediction have shown toreflect statistics of the environment; e.g. the probability that a memory
will be needed correlates to the frequency of prior exposures (Anderson &Schooler, 1991). Sensitivity to relevant world statistics has been shown to
guide cognitive judgment (e.g. Griffiths et al. (2006))
Hence, computationally and statistically we can emulate human’s language ability using statistical inferred language model that assigns probability to sequence of words. The computational language modeling is achievedby:(i) creating a probability distribution that can reflect humans’ use of language,(ii) calculating the probability of inferring certain psycholinguistically motivated language phenomena and(iii) predicting language phenomena from the statistical inference when
encountering new language data.

31

CHAPTER 5. TOPIC MODELS AND NLP

32

Iyer & Ostendorf (1999) posited that a topic can be represented as a probability distribution over words. For example, given the following sets ofwords (and its probability), we can infer that first set of words refer to the
biological plant and the latter, industrial plant.
(0) {water (0.038), fertilizer (0.027), grow (0.026), tree(0.024), organic(0.019),
industrial (0.014), pollution(0.009), strike(0.003)}(1) {water (0.038), industrial (0.027), fertilizer (0.026), pollution(0.024),
grow (0.019), strike(0.014), tree(0.009),organic(0.003)}
We refer to each set of words as a topic, z. And each document is a mixtureof a fixed set of topics with varying probability, we refer to this mixtureof topics per document as the gist, g. From a lexical semantic perspective
we can perceive a topic as a cluster of related percepts (which may or may
not pertain to any particular concept). And each document has varying
possibilities of falling into different topics.
Hofmann (1999) pioneered the extraction of topics from large unannotated corpora using probabilistic latent semantic indexing (PLSI). Blei et al.
(2003b) proposed generative topic modeling for collections of documents using Latent Dirichlet Allocation (LDA) by the introduction of a Dirichletprior on the distribution over topics.
Other studies on using generative methods for topic induction includes
Parametric Mixture Models (Ueda & Saito, 2006), variational Expectation
Maximization (vEM) (Buntine, 2002); Chinese Restaurant Process (Bleiet al., 2003a) Markov Chain Monte Carlo (MCMC) (Buntine & Jakulin,2004), Gibb Sampling (Griffiths et al., 2007), etc.
The next section of this chapter describes the process in generating andinferring topic models using Latent Dirichlet Allocation (LDA).

CHAPTER 5. TOPIC MODELS AND NLP

5.1

33

Topic Models with Latent Dirichlet Allocation

Latent Dirichlet Allocation is the process of learning N topics from a text
corpus and assigning the probability of every topics to each document1 fromthe corpus.
The generative language models assume that in human language, latentsemantic structures, l, generates words, w. Topic modeling posits that theselatent semantic structures are in the form of gists. LDA’s definition of topic
model assumes that a document is generated by allocating a distribution
over topics for each document’s gist and then allocating the probability ofeach word given a topic determined by the gist (Blei et al., 2003b).
Formally, we define LDA as such. Given a multidocument corpus, ofk size, expressed as vector of words w = {w1 ,...,wn }, where wi belongs to
di in a word-document co-occurrence matrix, the gist, g, is multinomiallydistributed over T topics with θ(d) (i.e. the probability of a particular topic
occurring in document d for a given word) parameters, approximated to α
under the Dirichlet distribution. The topics are represented by a multino(z)mial distribution over words in the vocabulary, with φw (i.e. the probabilityof a particular word occurring in a particular topic), approximated to β under the Dirichlet distribution.
P(z |g) = θ(d) e Dirichlet(α)(z)

P(w |z ) = φw e Dirichlet(β)
In other words, the distribution of topics over words is assumed to be prior
on a Dirichlet distributed alpha parameter. LDA users can specify the αand β hyperparameters that will affect the granularity of topics induced by
the model (Griffiths & Steyvers, 2004).

Figure 5.1: Graphical Representation of the Latent Dirichlet Allocation,
where M = no. of documents and N = no. words per document1
Depending on the NLP task, a document can be as short as one sentence, a paragraphor as long as a full text

CHAPTER 5. TOPIC MODELS AND NLP

34

Below illustrate the intermediate outputs of the LDA topic modeling andVariational Bayesian topic inference. Firstly the a topic model is created byinducing a user-defined number of topics (e.g. no. of topics = 2).
Sample Corpus:
i. The trees grow with organic fertilizer and water
ii. The workers at the industrial plant that caused pollution are on strikeiii. That industrial plant manufactures fertilizer
iv. Remember to water the tree regularly
Each topic consist of feature words that are asummed to be describing the
topic and each feature word is assigned a probability through the Latent
Dirichlet Allocation process (Griffiths & Steyvers, 2004).
Induced Topics:
topic0: {water (0.038), fertilizer (0.027), grow (0.026), tree(0.024), organic(0.019),
industrial (0.014), pollution(0.009), strike(0.003)}
topic1: {water (0.038), industrial (0.027), fertilizer (0.026), pollution(0.024),
grow (0.019), strike(0.014), tree(0.009),organic(0.003)}
Finally, using the induced topics, the model infers the probability of a topic
occuring for each document in the sample corpus. The probability is calculated using Variational Bayesian inference (Hoffman et al., 2010).
Document Gists:
i. (topic0 , 0.84) , (topic1, 0.16)
ii. (topic0 , 0.36) , (topic1, 0.64)
iii. (topic0 , 0.19) , (topic1, 0.81)
iv. (topic0 , 0.72) , (topic1, 0.28)

Chapter 6

Matching Query Sentence toParallel Corpus using Topic
Models for WSD
The XLING system introduces a novel approach to WSD by (i) first finding
the closest match to the query sentences from sentences in a parallel corpus
using topic models and it returns the word alignments as the translation for
the target polysemous words, (ii) then using the word-translation pair wefind the relevant sense ID to complete the sense disambiguation task. We
call the first subtask (i) as the match and translate step and subtask (ii)as the map step.
This chapter examines the Topical CLWSD task by describing (i)XLING
system’s participation in the SemEval-2013 Crosslingual Word Sense Disambiguation (CLWSD) task (section 6.1 to 6.5), (ii) mapping the CLWSD
outputs to WordNet senses and evaluating the overall accuracy of the Topical CLWSD approach (section 6.6). Thereafter this chapter concludes withdiscussion and the conclusion for the Topical CLWSD task. (section 6.7 6.8)

35

CHAPTER 6. TOPICAL CLWSD

6.1

36

Background and Hypothesis

Topic modelling assumes that latent topics exist in texts and each semantic
topic can be represented with a multinomial distribution of words and eachdocument can be classified into different semantic topics (Hofmann, 1999).
Blei et al. (2003b) introduced a Bayesian version of topic modeling using
Dirichlet hyper-parameters, Latent Dirichlet Allocation (LDA). Using LDA,a set of topics can be generated to classify documents within a corpus. Each
topic will contain a list of all the words in the vocabulary of the corpus witheach word is assigned a probability of occurring given a particular topic (seechapter 2.2.3.2 and 3.2.3.2 for more detailed description of topic modelingand LDA)We hypothesize that sentences with different senses of a polysemous word
will be classified into different topics during the LDA process. By matching
the query sentence to the training sentences by LDA induced topics, themost appropriate translation for the target polysemous word in the query
sentence should be equivalent to translation of the word in the matchedsentence(s) from a parallel corpus.

6.2

System Description

The XLING TnT system attempts the matching subtask in three steps (1)Topicalize:
matching the query sentence to the training sentences by the most probabletopic. (2) Rank: the matching sentences were ranked according to the cosine similarity between the query and matching sentences. (3) Translate:provides the translation of the polysemous word in the matched sentence(s)from the parallel corpus.

6.2.1

Preprocessing

The Europarl corpus bitexts1 (see chapter 4.1.1.3) were aligned at word-levelwith the GIZA++ statistical word alignment tool. The translation tables fromthe word-alignments were used to provide the translation of the polysemous
word in the Translate step.

1

eng-deu, eng-spa, eng-fre, eng-ita, eng-nld

Figure 6.1: XLING system flowchart: Training (section 6.2.1 to 6.2.2.2)

CHAPTER 6. TOPICAL CLWSD
37

Figure 6.2: XLING system flowchart: Testing (section 6.2.2.3 to 6.2.4)

CHAPTER 6. TOPICAL CLWSD
38

CHAPTER 6. TOPICAL CLWSD

39

The English sentences from the bitexts were lemmatized using a dictionarybased lemmatizer (xlemma).2 The lemmatizer used WordNet entries as alemma dictionary and it preferred tokens with plural if the plural and singular form of a word achieves two different synsets. For example, xlemma preferred spectacles (04272054-n) to spectacle (00075471-n, 06889138-n, 04271891n). Before and after the lemmatization, English stopwords were removed
from the sentences. The lemmatized and stop filtered sentences were used
as document inputs to train the LDA topic model in the Topicalize step.
Previously, topic models had been incorporated as global context features into a modified naive Bayes network with traditional WSD features(Cai et al., 2007). We try a novel approach of integrating local context (Ngrams) by using pseudo-word sentences as input for topic induction. For
example:Original Europarl sentence:“If players fail to score, their coach does not go and widen the goal,
but instead sees to it that they play better.”Lemmatized and stopped:“player fail score coach go widen goal see play better
Ngram pseudo-word:“player fail score fail score coach score coach go coach go widen
go widen goal widen goal see goal see play see play better”

6.2.2

Topicalize

The Topicalize step of the system first (i) induces a list of topics using
LDA, then (ii) allocates the topic with the highest probability to each training sentence and finally (iii) the topic is inferred from the query sentence
and a list of sentences that shares the same top ranking topic are considered
as matching sentences that contains the target polysemous word with thesame sentence
6.2.2.1 Topic Induction
Topic models were trained using Europarl sentences that contain the singular or plural form of the focus word. The topic models were induced using2

https://code.google.com/p/xlemma/

CHAPTER 6. TOPICAL CLWSD

40

LDA by setting the number of topics (#topics) as 50, and the alpha andbeta hyper-parameters were symmetrically set at 1.0/#topics.3 For example, topic 1 is more relevant to the human coach (09931640-n) whereas
topic 2 is more relevant to the vehicular coach (02924116-n).
Topic 1 : [(0.0208, ’sport’), (0.0172, ’player ’), (0.0170, ’train’),(0.0166, ’league’), (0.0133, ’field ’), (0.0133, ’football ’),(0.0130, ’bus’), (0.0117, ’drive’), (0.0117, ’transport’),(0.0130, ’tour’), (0.0117, ’departs’), (0.0117, ’goal ’),(0.0111, ’carriage’)]
Topic 2 : [(0.0208, ’bus’), (0.0172, ’drive’), (0.0170, ’train’),(0.0166, ’departs’), (0.0133, ’tour ’), (0.0133, ’football ’),(0.0130, ’player’), (0.0117, ’team’), (0.0117, ’transport’),(0.0130, ’carriage’), (0.0117, ’horse’), (0.0117, ’team’),(0.0111, ’sport’)]
6.2.2.2 Topic Allocation
Each sentence was allocated the most probable topic induced by LDA. An
induced topic contained a ranked list of tuples where the 2nd element ineach tuple was a word that associated with the topic, the 1st element was
the probability that the associated word will occur given the topic.
6.2.2.3 Topic Inference and Match
With the trained LDA model, each query sentence was fitted into the topic
model to infer the probability of every induced topics given the query sentence. Using the most probable topic of each query sentence, sentences fromthe training corpus that shares the same top ranking topic was extracted.
The top 10 training sentences that shared the top ranking topic were considered as matching sentences. The topic induction, allocation and inference
were done separately on the lemmatized and pseudo-word sentence, resulting in two set of matching sentences. Only the sentences that were output
from both set of matches are considered for the Rank step.
3

Blei et al. (2003b) had shown that the perplexity plateaus at when #topics ≥ 50;
higher perplexity meant more computing time needed to train the model.

CHAPTER 6. TOPICAL CLWSD

6.2.3

41

Rank

A mini dictionary was built from the matching sentences output by Topicalize step and the documents were normalized using term frequency-inverse
document frequency (tf-idf). Then the matching sentences were ranked according to the cosine similarity with the query sentences. Only the top fivesentences were piped into the Translate step.

6.2.4

Translate

From the matching sentences, the translate step checks the GIZA++ (Och& Ney, 2003) word alignment table and outputs the translation(s) of thetarget polysemous word. Each matching sentence could output more than
1 translation depending on the target word alignment. As a simple way offiltering stop-words from target European languages, translations with lessthan 4 characters were removed. This effectively distilled misaligned noncontent words, such as articles, pronouns, prepositions, etc. To simplify thelemmatization of Spanish and French plural noun suffixes, the -es and -s
were stemmed from the translation outputs.
The XLING TnT system output one translation for each query sentences
for the best result evaluation and outputs the top 5 translations for theout-of-five result evaluation.

6.2.5

Fallback

For the out-of-five evaluation, if the query returned less than 5 answers, thefirst fallback appends the lemmas of the Most Frequent Sense (according to
SemCor) of the target polysemous word in the respective language WordNets. The second fallback appended the most frequent translations of thetarget polysemous word to the queries response.

6.2.6

Baseline

Instead of matching sentences by topic models, we tried a simplistic baseline
for matching sentences by cosine similarity between the lemmas sentence ofthe query sentence and the lemmas of each English sentence in the training
corpus. The baseline system was named XLING SnT (Similar and Translate).
The cosine similarity was calculated from the division of the vector productCHAPTER 6. TOPICAL CLWSD

42

of the query and training sentence (i.e. numerator) by the root product ofthe vectors magnitude squared.

6.3

CLWSD Task Evaluation

To empirically evaluate the matching subtask, we subscribed to the Crosslingual Word Sense Disambiguation (CLWSD) task from SemEval-2013 where1000 queries from 20 polysemous:• coach• letter• post• scene• education• match• pot• side• execution• mission• range• soil• figure• mood• rest• strain• job• paper• ring• testTo score the system outputs, the CLWSD used the classic precision score4
the mode precision from SemEval-2007’s lexical substitution task (McCarthy
et al., 2007)5 . The mood precision was calculated based on the majority
choice of translation that human annotators have selected. Let H be a setof human annotators, T be the set of query items with hi as the set of
responses for each query for each query i ∈ T for annotator h ∈ H. For eachi ∈ T, we calculated the mood (mi ) which corresponds to the translation
with highest ranked translation from H. Let A be a set from T where thesystem provides at least one answer and ai : i ∈ A be the set of answers
from the system for each query, i. For each i, we calculated the multiset
union (Hi ) for all hi from all H. And for each unique answer (res) from Hi
that has an associated frequency (freqres ). Three annotators were required
to choose the top 3 translation of the polysemous target word for each query
sentence, and the freqres =1 if translation is picked by 1 annotator, freqres =2
if by 2, and freqres =3 if by 3 annotators.
The CLWSD task was evaluated on two sets of results best and outof-five (oof ). For the best evaluation, there was no limit on how manytranslation(s) the system can provide for the target polysemous word, but
the score was divided by the number of answers. For the oof evaluation,
4
5

precision = true positive / (true positive + false negative)
The CLWSD refer to mode precision as mood precision or moodCHAPTER 6. TOPICAL CLWSD

43

systems can provide up to five answers and the score was not divided by the
number of guesses.

6.4

CLWSD Results

12 teams registered for the CLWSD task evaluation in SemEval-2013, 5
teams completed and submitted their evaluation results. Table 1 and 2
presents the results for the XLING system for best and out-of-five evaluation. Our system did worse than the tasks baseline, i.e. the Most FrequentTranslation (MFT) of the target word for all languages. Moreover the topic
model based matching did worse than the cosine similarity matching baseline. The results clearly disproved our hypothesis that sentences with different sentences with different senses of a polysemous word will be classified
accordingly by topics during the LDA process.
Li et al. (2010) and Anaya-Sánchez et al. (2007) had shown that pure
topic model based unsupervised system for WSD should perform a little
better than Most Frequent Sense baseline in coarse-grained English WSD.
Hence it was necessary to perform error analysis and tweaking to improve
the XLING system. Moreover, the XLING underperforms compared to othersystems which completed the CLWSD task (see fig. 6.5).6

6.5

Error Analysis

Hyperparameters Modification
We also explored why the topic model based matching performed worse thansurface cosine similarity matching. Statistically, we could improve the ro6

ParaSense was the organizers’ system

CHAPTER 6. TOPICAL CLWSD

44

Figure 6.3: Precision and (Mode) Precision for best Evaluation

Figure 6.4: Precision and (Mode) Precision oof Evaluation

Figure 6.5: Mood Precision for best Evaluation of Competing Systems
bustness of the topic models by (i) tweaking the Dirichlet hyper-parameters
to alpha = 50/#topics, beta = 0.01 as suggested by Wang et al. (2009).
Although the hyperparameter tweaks improved the scores for German
and Dutch evaluations but the tweak brought the overall precision and mood
precision of the other three languages down. Since the documents from eachCHAPTER 6. TOPICAL CLWSD

45

language were parallel, this poses the need to explore the level of languagedependency for LDAs hyperparameters.

Figure 6.6: Evaluation Results of the TnT with Hyperparameter tweaks
Given no improvement from hyper-parameter tweaks, we reiterated BoydGraber et al.’s (2007) assertion that while topic models capture polysemous
use of words, they do not carry explicit notion of senses that is necessary
for WSD.
Data Sparsity and Redundency
Other than the hyperparameter issue, there were a couple of fail points thatcame from training data (a) data sparsity causing topic model fail and (b)topic drift that resulted in redundant overtraining (see Appendix A).
Across all languages, the queries for the polysemous word pot had retrieved poor precision (<5%), it is because the Europarl corpus had only
80-90 sentences with the word pot. One possible solution is to include sentences that contains synonyms of pot with only one sense.
However more data does not mean it is useful in training the topic models, e.g. for the Spanish evaluation of XLING TnT (Figure A.2 from appendix), the model for the polysemous job (14 concepts) was trained from10,000 documents while test (13 concepts) was trained with 2,800 documents but the XLING TnT system scored 14.25% for job and 34.94% for test.
We investigated the training document-precision correlation and when the
topic model was overtrained, it caused instability in topic weighting andinference, it was not unlike the effects of topic drift in online LDA topic
modeling (Hoffman et al., 2010).

Figure 6.7: Overtraining: no. of Document-Precision Scatterplot

CHAPTER 6. TOPICAL CLWSD
46

CHAPTER 6. TOPICAL CLWSD

47

Figure 6.5 showed that precision falls below the baseline when model istrained with more than 3,000 training documents. This phenomenon wascaused by our unconventional use of topic models; usually, topics were minedfrom large collection of documents and the topics were induced to represent
the different topics that corpus represents, but we used LDA to induce finer
grain topics that supposedly would fall into one single topic if mined from alarge collection corpus. Thus, the hyperparameters, that determines granularity of topics, would differ and the optimal training corpus size would not
be the presumed as large as possible size.
Not unlike various machine-learning statistical language modeling, thenumber of parameters, LDA has 50 free parameters7 that the user can specify
to train the topic model. Hence, finding the optimal values of each parameterrequires much trial and error or mathematical estimation specific to thetraining data and task requirements.

6.6

From CLWSD to Classic WSD

The second task of Topical CLWSD is to map the word-translation pair
provided by the matching subtask to the Open Multilingual WordNet andreturn evaluate the answers based on whether their WordNet mapping are(i) a subset of the gold answer WordNet mapping (insense) or (ii) exactly
the same as the gold answer mapping (samesense). This second task was
only performed on the Romance languages (Spanish, French and Italian).
For example, given the query sentence, Q:“Then it’s off aboard the coach into the reserve’s safari park to see theseanimals, as well as others, roaming freely.”
The XLING system returns the best translation, autobus. And the Spanish gold answers for the query is (autobus and autocar ). We map the gold
answers to the OMW and produces goldsense, 02924116-n and since themapping from the system output only returns 1 sense, the insame and samesense score is both 100%. The insame and samesense score average perpolysemous word per language is presented on figure 6.6.

7
To name a few, #topics, α, β, random seed value to initialize the generative process(i.e. Expectation Maximization for LDA), etc.

Figure 6.8: CLWSD to Classic WSD

CHAPTER 6. TOPICAL CLWSD
48

CHAPTER 6. TOPICAL CLWSD

49

Another example with query sentence:“While the test results for physical search exceeded the national average,
both the metal detector and X-ray results were below average.”
The system answer is prueba which returns three possible senses {00791078n,05799212-n,00794367-n}. And the gold answers (ensayo;experimento; inspeccion;investigacion;prueba;test) returns 5 possible senses {07197021-n,
01006675-n, 00791078-n, 05799212-n, 00794367-n}. The insense score would
be 0.6 while the samesense score would be 0.
Figure 6.6 showed getting the same sense remains a challenge due to thelow precision of the matching task. The insense and samesense scores for
French and Italian were lower than the Spanish because the possibility ofmapping the gold answers provided by the CLWSD to the OMW is lower (seepossible query in figure 6.6). Given sufficient coverage of the WordNet, it
might be possible to achieve sense mappings that produce samesense scoresthat are close to the CLWSD precision of the system.
By disregarding the unmapped senses, we partially overcame the lack ofcoverage of the target language WordNets but failed to capture correct senses
answered by the system that were not in the OMW. The previous insense andsamesense score can be comparable to performance of systems participatingin the CLWSD task. To make the traditional precision, recall and f-score
measure comparable to classic WSD task8 , we excluded the queries that did
not return sense mapping from the OMW and take the samesense counts
as true positive (tp), wrong answers as the false positive (fp). If the system
outputs a senseID although the gold translation could not, it also counted
towards false negative (fn) score.

Figure 6.7 presented the precision, recall and f-score on the adapted classic
WSD task using gold translation mappings from the OMW as the gold
8
more specifically, classic coarse-grain WSD, since the mapping a gold tranlsation from
CLWSD to OWN produces more than one senseID

CHAPTER 6. TOPICAL CLWSD

50

Figure 6.9: Topical CLWSD score (comparable to Classic WSD)
senses and the OMW mappings of the XLING TnT outputs from the previous
CLWSD task. This OMW mapped WSD subtask is very much dependent on
the performance of the system in the CLWSD task as well as the coverage ofthe various target language WordNets. For the French queries, we achieved
high precision because the number of mappings from the gold translationsof the CLWSD to the OMW is almost halved the number of all the possible
queries.9 . For the Italian queries, we scored low on both precision and recall
mainly due to the lack of precision in the CLWSD task.
The results showed that by crosslingual approach to WSD was able toachieve high precision score and reinstated the usage of parallel texts to
reduce the sense ambiguity of polysemous words. However the recall remains low due to number of false negatives, mainly partial answers from
insense that XLING system provides; i.e. mainly because the outputs fromthe CLWSD substask could not be mapped to the Open Multilingual WordNet.

6.7

Discussion

The main advantage of statistical language independent approaches is theability to scale the system in any possible language. However language dependent processing remains crucial in providing accuracy to the system,
especially lemmatization in WSD tasks (e.g. kraftomnibusverkehr ). Moreover, disambiguating senses solely from sentential context is artificially hard.
By going through the individual queries and responses from the matching
substask, we identified several issues in the translate step that needed tobe resolved to achieve higher precision. Using language specific lemmatiz9
The maximum number of possible queries is 1000, all 50 queries for each of the 20
polysemous words in the CLWSD task

CHAPTER 6. TOPICAL CLWSD

51

ers, as other competing systems for the CLWSD task had done, would had
improved the accuracy of the system. For example:(i) German-English and Dutch-English word alignments containing compound words needed to be segmented (e.g. kraftomnibusverkehr needed
to be segmented to kraft omnibus verkehr and realigned such that thetarget word coach only aligns to omnibus),(ii) de-pluralization of Italian, German and Dutch was crucial was getting
the gold answers of the task (e.g. XLING answers omnibussen while
the gold answers allowed omnibus).

6.8

Conclusion for Topical CLWSD

This thesis had defined the Topical CLWSD task by first finding a match
to the query sentence from a parallel corpus and disambiguating the senses
from the interlinked Open Multilingual Wordnet (OMW). The topic-model
based sentence matching fails to meet the Most Frequent Translation (MFT)baselines. But the surface cosine baseline, without any incorporation of any
sense knowledge, had surprisingly achieved performance closer to MFT. The
surface cosine similarity measure could serve as a baseline for the sentence
matching subtask in future research.
Although the sentence matching subtask required much attention for future research, the disambiguation of senses from OMW has shown potential
in WSD if query sentences from the WSD evaluation tasks contain not justthe English sentences but also their translations in multiple languages.

Chapter 7

Nanyang Technological
University - Multilingual
Corpus (NTU-MC)1
WSD is not a stand-alone task and neither is the XLING system. The
XLING system serves as one of the language disambiguation devices in
the quest to understand human language (syntactically and semantically)
crosslingually. The pursuit of crosslingual language technologies is based on
the realization that parallel/translated/comparable texts from linguisticallydiverse languages provide more information in human language understanding for NLP tasks. Thus, crosslingual NLP researches focus on (i) improving
multilingual data size and annotations and (ii) extracting disambiguatingknowledge from multilingual data.

7.1

Linguistically Diverse Corpus

The NTU-MC is a linguistically diverse corpus that contains 595,000 words(26,000 sentences) in 7 languages (Arabic, Chinese, English, Indonesian,
Japanese, Korean and Vietnamese) from 7 different language families (AfroAsiatic, Sino-Tibetan, Indo-European, Austronesian, Japonic, Korean (alanguage isolate) and Austro-Asiatic). The current version of the NTU-MC
consists of text from two subcorpora (yoursingapore and singaporemedicine).

1

Earlier versions of this chapter appear in Tan & Bond (2011, 2012)52

Figure 7.1: A sample of monolingual annotation from yoursingapore

CHAPTER 7. NTU-MC
53

Figure 7.2: A sample of monolingual annotation from singaporemedicine

CHAPTER 7. NTU-MC
54

CHAPTER 7. NTU-MC

55

The NTU-MC is annotated with a layer of monolingual annotation (POS
and sense tags) and crosslingual annotation (sentence level alignments). The
diverse language data and crosslingual annotations provide valuable information on linguistic diversity for traditional corpus linguistic research aswell as natural language processing tasks such as Machine Translation. The
NTU-MC is an on-going effort to compile and redistribute machine-readable
and quality annotations from parallel/translated texts with linguistically diverse languages.

7.1.1

Historical log

The first version of the NTU-MC consisted of the foundation texts fromSingapore Tourism Board’s (STB) website (www.yoursingapore.com). It
was built in May 2011 with 375,000 words (15,000 sentences) in 6 languages
from 6 different language family trees. Part-of-Speech annotations were
included in various degrees of accuracy. Other than being machine-readable,
the corpus was designed to suit the Corpus Query Processor web (CQPWeb)
graphic user interface.
The second version of the NTU-MC (Dec, 2011) moves on from monolingual POS annotations to provide crosslingual sentence alignments useful for
multilingual NLP tasks such as machine-translation or language detection.
English-Chinese and English-Japanese bitexts were manually aligned at sentence level and the other language pairs were automatically using HunAlign(Varga et al. 2005).
The third version of the NTU-MC (May, 2012) had an increased size withtexts from STB’s medical tourism website (www.singaporemedicine.com).
With the inclusion of the new subcopus, the NTU-MC grew to 595,000
words (26,000 sentences) with the addition of Arabic texts from the newsubcorpus. We have also implemented a probabilistic Bahasa Indonesian
POS-tagger with the specifications recommended by Pisceldo et al. (2010).
The forth version of the NTU-MC (Jan, 2013) focused on improving
annotations’ accuracy. The yoursingapore subcorpus was manually sensetagged (40,000 concepts for each language) in English, Chinese, Indonesian and Japanese. To resolve mis-segmentation issues as reported in theprevious versions of NTU-MC, a dictionary of Singaporean street names,
train-stations and landmarks were created with a dictionary-based Chinesesegmentation tool (minisegmenter).

CHAPTER 7. NTU-MC

56

The fifth version of the NTU-MC is scheduled to be release in September
2013 with automatically sense-tagged annotations with the XLING system,
as well as the merger of two new subcorpora (’dancing man’ and ’cathedraland bazaar’).

7.2

Asian Language NLP tools

Along with the corpus compilation, NLP tools were created to annotate thecorpus. Although NLP tools for Asian Languages are available, they are
seldom accessible nor given attention and often lack documentation.

7.2.1

Indonesian POS tagger

IndoTag is a probabilistic Conditional Random Field (CRF) Bahasa Indonesian Part of Speech (POS) tagger with the specifications recommended by
Pisceldo et al. (2010). The pre-trained model is based on the unigram CRF
with 2-left and 2-right context features using the Universitas Indonesia’s 1
million word corpus compiled under the Pan Asia Networking Localization(PANL10N) project.2 The IndoTag achieved 78% accuracy in a text sample
POS evaluation of the NTU-MC.

7.2.2

Mini-segmenter

Mini-segmenter3 (mini-segmenter) is a lightweight lexicon/dictionary based
Chinese text segmenter; it adds whitespace to separate and tokenize thetext. The advantage of using a lexicon/dictionary for text segmentationis the ability to localize and scale according to the text’s language or domain. Supporting the open source movement, the default dictionary usedby mini-segmenter is MDBG’s (2013) CC-CEDICT.
The mini-segmenter first generates all possible combinations of tokens
using the dictionary and ranks the combinations according to a ad-hoc scoring system (i.e. mini-square score). It is calculated using the summationof the square of the length of each segment. This novel scoring is based on
the preference for larger chunks than smaller chunks in a sentence.
2

PANL10N project is an initiative to build capacity in regional institutions for local
language computing in South and South-East Asian countries
3
The mini-segmenter at https://code.google.com/p/mini-segmenter/

CHAPTER 7. NTU-MC

7.2.3

57

GaChalign

GaChalign4 is a python implementation of Gale-Church’s (1993) lengthbased sentence aligner with options for variable parameters (viz. mean,variance, penalty). The aim of the tool development was to address theissue of poor sentence alignment between logographic/syllabic languages toalphabetic languages. Our experiment with English-Japanese NTU-MC bitext has shown that:• aligning a syllabic/logographic language (JPN) to an alphabetic language (ENG) remains a challenge for Gale-Church algorithm (f-score
peaks at 62.9%)• using the calculated character mean5 from the unaligned text improves
precision and recall of the algorithm• using the calculated alignment type penalties from a sample gold corpus also improves fscore

4
5

GaChalign is freely available at https://code.google.com/p/gachalign/i.e. the ratio of source language characters to target language charactersChapter 8

Conclusion and Future Work
Language is ambiguous and understanding language ambiguity computationally remains a challenge for the current state-of-art technologies. In thisthesis we attempted to resolve language ambiguity through the Word Sense
Disambiguation (WSD) task using parallel text and statistical topic models.
Different from previous approaches to WSD, we explore the disambiguating
power of using parallel text in the WSD task by (i) first finding a match to an
English sentence with a polysemous word from a parallel corpus and extract
the tranlsation of the polysemous word from the corpus’ word-alignment(match and translate) then (ii) mapping the word-translation pair to theOpen Multilingual WordNet (OMW) (map) and evaluate the sense disambiguation system base on the OMW sense inventory. We named the task
topical CLWSD.
The XLING system was built to attempt the topical CLWSD task. Although the XLING system performed under the Most Frequent Translation(MFT) baseline in the first sentence-matching subtask evaluation1 , but it
was able to achieve high precision on the OMW-mapped WSD subtask.
Future research on WSD and meaing disambiguation should further exploit the use of parallel translations to reduce the sense disambiguity as wehave shown in the OMW-mapped WSD subtask. Crosslingual knowledge
become increasingly important in the field of machine translation wherecrosslingual semantic/syntactic information has proven to improve performance.
1
We adopt the SemEval-2013’s Crosslingual WSD (CLWSD) evaluation and to evaluate
the sentence-matching subtask

58

CHAPTER 8. CONCLUSION AND FUTURE WORK

59

Other than computationally disambiguating word senses, this thesis updated the readers on the ongoing work in compiling the linguistically diverse
Nanyang Technological University - Multilingual Corpus (NTU-MC). Boththe XLING and the NTU-MC are related in their attempts to build crosslingual technologies.
Future Work
It is important to note that the thesis’ approach to WSD involved minimal
usage of knowledge resources and preprocessing tools; the XLING systemused an aligned parallel corpus, an English lemmatizer and two sentence
similarity measures (topic based matching and cosine similarity) and theOpen Multilingual WordNet. Topical CLWSD’s (i) match and translateand (ii) map approach to WSD shows potential when we consider the highprecision that can be yielded by the map step (i.e. generating the overlapping
synsets of the polysemous words and their matched translations).
Future work on the match, translate and map approach should attend
to the low precision and mood of the match step, where the CLWSD systemfinds a match to the context sentence from a parallel corpus.
In the Topical CLWSD experiment, sentences were allocated only the top
ranking LDA induced topic. In the light of the results, using the full topic
distribution to compute similarities between queries and the training corpus
might provide a richer representation to better capture sense distinction andimprove the results.
The XLING system used sentential cosine similarity measure to findthe sentences from the training corpus that are semantically similar to thecontext sentence. It may be beneficial to compare various groupwise semantic similarity measure (e.g. Jaccard index, Normalized Google distance) to
determine which measure is most apt for the CLWSD task.
Other than improving the similarity matching measure, it might be simpler to consider using pre-existing paraphrasing software (e.g. SEMILAR)
to assess the similarity between the context sentence and the corpus.

Appendix A

Appendix A presents the detailed CLWSD evaluations for the XLING systemper query per language. The content of appendix A includes:• CLWSD Evaluation for XLING TnT system (German)• CLWSD Evaluation for XLING TnT system (Spanish)• CLWSD Evaluation for XLING TnT system (French)• CLWSD Evaluation for XLING TnT system (Italian)• CLWSD Evaluation for XLING TnT system (Dutch)• CLWSD Evaluation for XLING SnT system (Germanic: de,nl)• CLWSD Evaluation for XLING SnT system (Romance: es,fr,it)

60

APPENDIX A. DETAILED CLWSD EVALUATIONS

Figure A.1: CLWSD Evaluation for XLING TnT system (German)

61

APPENDIX A. DETAILED CLWSD EVALUATIONS

Figure A.2: CLWSD Evaluation for XLING TnT system (Spanish)

62

APPENDIX A. DETAILED CLWSD EVALUATIONS

Figure A.3: CLWSD Evaluation for XLING TnT system (French)

63

APPENDIX A. DETAILED CLWSD EVALUATIONS

Figure A.4: CLWSD Evaluation for XLING TnT system (Italian)64

APPENDIX A. DETAILED CLWSD EVALUATIONS

Figure A.5: CLWSD Evaluation for XLING TnT system (Dutch)

65

Figure A.6: CLWSD Evaluation for XLING SnT system (Gothic)

APPENDIX A. DETAILED CLWSD EVALUATIONS
66

Figure A.7: CLWSD Evaluation for XLING SnT system (Romance)

APPENDIX A. DETAILED CLWSD EVALUATIONS
67

Appendix B

Appendix B documents the gotcha moments in coding in python for Multilingual Natural Language Processing (NLP). Often these problems are classified as too localized on online forums and hence they tend to be ignored.
Solution documented in this appendix works for Python 2.7.4 and above but
it does not work on Python 3.0+.

68

APPENDIX B. GOTCHA CODE SNIPPETS

B.1

69

UTF-8 Processing in Python

As a convention to processing Universal Character Set Transformation Format - 8 bits (utf-8) textfiles, it is polite to inform the python interpreter todeclare its python source code encoding to utf-8, otherwise the interpretercomplains with errors when executing the code.1 Declaration of soure code
encoding can be done at the first line of the textfile. However, even if you
have declared source code encoding at the start of your code, you might stillencounter UnicodeEncodeError when you have unicode characters in yoursource code, e.g.:

This issue of setting default python encoding was discussed in depth at Why
we need sys.setdefaultencoding(utf-8) in a py script? .

1

for more detailed PEP, see http://www.python.org/dev/peps/pep-0263/

APPENDIX B. GOTCHA CODE SNIPPETS

B.2

70

Normalizing Accented Latin Characters

Often in processing European languages we want to remove diacritics oraccent marks from the tokens, for example, converting from téléportation toteleportation. More often than not, the original token with diacritics causes
problem when it is fed as input to preprocessing tools. To normalize diacritic
in python, you can use the normalize module in the unicode library as such:

The option NFKD stands for Normal Form Compatible Decompose. By specifying the NFKD, python replaces all compatibility characters with their equivalents, e.g. accented é to e. For more details, see the official documentationfrom python API for unicode.normalize().

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