IALP 2011 Gunawan Jessica Hariadi Ketut
2011 International Conference
on Asian Language Processing
IALP 2011
Table of Contents
Message from the General Chair ..........................................................................................................................x
Message from the Program Chairs......................................................................................................................xi
Message from the Local Organizing Chair........................................................................................................xii
Conference Committees......................................................................................................................................xiii
Program Committee............................................................................................................................................xiv
Invited Talks.........................................................................................................................................................xvi
Phonology, Morphology, Syntax, and Language Model
A Simplified-Traditional Chinese Character Conversion Model Based
on Log-Linear Models .............................................................................................................................................3
Yidong Chen, Xiaodong Shi, and Changle Zhou
Improving Chinese Dependency Parsing with Self-Disambiguating Patterns ........................................................7
Likun Qiu, Lei Wu, Kai Zhao, Changjian Hu, and Lingpeng Kong
Joint Decoding for Chinese Word Segmentation and POS Tagging Using
Character-Based and Word-Based Discriminative Models ...................................................................................11
Xinxin Li, Xuan Wang, and Lin Yao
Natural Language Grammar Induction of Indonesian Language Corpora Using
Genetic Algorithm .................................................................................................................................................15
Arya Tandy Hermawan, Gunawan, and Joan Santoso
Error-Driven Adaptive Language Modeling for Chinese Pinyin-to-Character
Conversion .............................................................................................................................................................19
Jin Hu Huang and David Powers
Theoretical Framework of Mongolian Word Segmentation Specification
for Information Processing ....................................................................................................................................23
Tong Laga and Xiaobing Zhao
Research on the Uyghur Information Database for Information Processing .........................................................26
Yusup Ebeydulla, Hesenjan Abliz, and Azragul Yusup
Sentence Boundary Detection in Colloquial Arabic Text: A Preliminary Result .................................................30
Afnan A. Al-Subaihin, Hend S. Al-Khalifa, and AbdulMalik S. Al-Salman
A Study of the Classification and Arrangement Rule of Uygur Morphemes
for Information Processing ....................................................................................................................................33
Pu Li and Shuzhen Shi
v
Graph-Based Language Model of Long-Distance Dependency ............................................................................37
Faguo Zhou and Xingang Yu
BASRAH: Arabic Verses Meters Identification System .......................................................................................41
Zainab A. Khalaf, Maytham Alabbas, and Tien-Ping Tan
Semantics
WordNet Editor to Refine Indonesian Language Lexical Database ......................................................................47
Gunawan, Jessica Felani Wijoyo, I. Ketut Eddy Purnama, and Mochamad Hariadi
Two Ontological Approaches to Building an Intergrated Semantic Network
for Yami ka-Verbs .................................................................................................................................................51
Meng-Chien Yang, Si-Wei Huang, and D. Victoria Rau
Issues with the Unergative/Unaccusative Classification of the Intransitive Verbs ...............................................55
Nitesh Surtani, Khushboo Jha, and Soma Paul
A Sentence-Level Semantic Annotated Corpus Based on HNC Theory ...............................................................59
Zhiying Liu, Yaohong Jin, and Chuanjiang Miao
An Exploration on the Modes of Word Meaning Extension Based on Metaphorical
and Metonymic Mechanisms .................................................................................................................................63
Xiaofang Ouyang
On the Semantic Orientation and Computer Identification of the Chinese Adverb
cai ...........................................................................................................................................................................67
Lin He and Pengbing Chen
Exploring Both Flat and Structured Features for Number Type Identification
of Chinese Personal Noun Phrases ........................................................................................................................71
Jun Lang
Discourse
The Context Imperative Sentences of Modern Chinese ........................................................................................77
Hao Zhao and Kaihong Yang
Research on Cross-Document Coreference of Chinese Person Name ...................................................................81
Ji Ni, Fang Kong, Peifeng Li, and Qiaoming Zhu
Research of Event Pronoun Resolution .................................................................................................................85
Ning Zhang, Fang Kong, and Peifeng Li
Co-reference Resolution in Vietnamese Documents Based on Support Vector
Machines ................................................................................................................................................................89
Duc-Trong Le, Mai-Vu Tran, Tri-Thanh Nguyen, and Quang-Thuy Ha
Research of Noun Phrase Coreference Resolution ................................................................................................93
Junwei Gao, Fang Kong, Peifeng Li, and Qiaoming Zhu
Discourse Structures of English Exposition ..........................................................................................................97
Donghong Liu and Meizhen Liao
Linguistic Competency Model for Intentional Agent ..........................................................................................101
Sivakumar Ramakrishnan and Vasuky Mohanan
vi
Text Understanding and Retrieval
The Comparison of Chinese Spam Filter Based on Generative Model
and Discriminative Model ...................................................................................................................................107
Yong Han, Yingying Wang, Huafu Ding, and Haoliang Qi
Applying Grapheme, Word, and Syllable Information for Language Identification
in Code Switching Sentences ...............................................................................................................................111
Yin-Lai Yeong and Tien-Ping Tan
An Integrated Approach Using Conditional Random Fields for Named Entity
Recognition and Person Property Extraction in Vietnamese Text .......................................................................115
Hoang-Quynh Le, Mai-Vu Tran, Nhat-Nam Bui, Nguyen-Cuong Phan,
and Quang-Thuy Ha
A Query Reformulation Model Using Markov Graphic Method ........................................................................119
Jiali Zuo and Mingwen Wang
Search Results Clustering Based on a Linear Weighting Method of Similarity ..................................................123
Dequan Zheng, Haibo Liu, and Tiejun Zhao
Extracting Pseudo-Labeled Samples for Sentiment Classification Using Emotion
Keywords .............................................................................................................................................................127
Sophia Yat Mei Lee, Daming Dai, Shoushan Li, and Kathleen Ahrens
Imbalanced Sentiment Classification with Multi-strategy Ensemble Learning ..................................................131
Zhongqing Wang, Shoushan Li, Guodong Zhou, Peifeng Li, and Qiaoming Zhu
Formalization and Rules for Recognition of Satirical Irony ................................................................................135
Lingpeng Kong and Likun Qiu
Summarization
An Automatic Linguistics Approach for Persian Document Summarization ......................................................141
Hossein Kamyar, Mohsen Kahani, Mohsen Kamyar, and Asef Poormasoomi
Context-Based Persian Multi-document Summarization (Global View) ............................................................145
Asef Poormasoomi, Mohsen Kahani, Saeed Varasteh Yazdi, and Hossein Kamyar
Centroid Integer Selection Model—A High Efficiency Method on Dynamic
Multi-document Summarization ..........................................................................................................................150
Meiling Liu, Dequan Zheng, Tiejun Zhao, and Yang Yu
Corpus Based Extractive Document Summarization for Indic Script .................................................................154
P. Vijayapal Reddy, B. Vishnu Vardhan, and A. Govardhan
Research on Multi-document Summarization Model Based on Dynamic
Manifold-Ranking ................................................................................................................................................158
Meiling Liu, Honge Ren, Dequan Zheng, and Tiejun Zhao
Machine Translation
An Orientation Model for Hierarchical Phrase-Based Translation ......................................................................165
Xinyan Xiao, Jinsong Su, Yang Liu, Qun Liu, and Shouxun Lin
vii
Improving Bilingual Lexicon Construction from Chinese-English Comparable
Corpora via Dependency Relationship Mapping .................................................................................................169
Hua Xu, Dandan Liu, Longhua Qian, and Guodong Zhou
A Rule-Based Source-Side Reordering on Phrase Structure Subtrees ................................................................173
Fangli Liang, Lei Chen, Miao Li, and Nasun-urtu
Automatic Acquisition of Chinese-Tibetan Multi-word Equivalent Pair
from Bilingual Corpora ........................................................................................................................................177
Minghua Nuo, Huidan Liu, Longlong Ma, Jian Wu, and Zhiming Ding
Character-Level System Combination: An Empirical Study for English-to-Chinese
Spoken Language Translation .............................................................................................................................181
Jinhua Du
Mining Parallel Data from Comparable Corpora via Triangulation ....................................................................185
Thi-Ngoc-Diep Do, Eric Castelli, and Laurent Besacier
Using Rich Linguistic and Contextual Information for Tree-Based Statistical
Machine Translation ............................................................................................................................................189
Bui Thanh Hung, Nguyen Le Minh, and Akira Shimazu
Research on Element Sub-sentence in Chinese-English Patent Machine Translation ........................................193
Zhiying Liu, Yaohong Jin, and Yuhuan Chi
Lexical Word Similarity for Re-ranking in Vietnamese-English Named Entity Back
Transliteration ......................................................................................................................................................197
Thi Hoang Diem Le and Ai Ti Aw
The Chinese-English Bilingual Sentence Alignment Based on Length ..............................................................201
Huafu Ding, Lili Quan, and Haoliang Qi
Optimal Translation Boundaries for BTG-Based Decoding ................................................................................205
Xiangyu Duan and Min Zhang
Spoken Language Processing
Acoustic Space in Motor Disorders of Speech: Two Case Studies .....................................................................211
Vaishna Narang, Deepshikha Misra, and Garima Dalal
Adopting Malay Syllable Structure for Syllable Based Speech Synthesizer for Iban
and Bidayuh Languages .......................................................................................................................................216
Sarah F. S. Juan, Vyonne Edwin, Chai Yeen Cheong, Jun Choi Lee, and Alvin W. Yeo
How Vietnamese Attitudes can be Recognized and Confused: Cross-Cultural
Perception and Speech Prosody Analysis ............................................................................................................220
Dang-Khoa Mac, Eric Castelli, Véronique Aubergé, and Albert Rilliard
Non-native Accent Pronunciation Modeling in Automatic Speech Recognition ................................................224
Basem H.A. Ahmed and Tien-Ping Tan
An In-car Chinese Noise Corpus for Speech Recognition ..................................................................................228
Jue Hou, Yi Liu, Chao Zhang, and Shilei Huang
An Experimental Study on Vietnamese Speech Synthesis ..................................................................................232
Liping Kui, Jian Yang, Bin He, and Enxing Hu
viii
Development of Acoustic Space in 3 to 5 Years Old Hindi Speaking Children .................................................236
Vaishna Narang, Garima Dalal, and Deepshikha Misra
Design of a Query-by-Humming System for Hindi Songs Using DDTW Based
Approach ..............................................................................................................................................................240
Prakhar K. Jain, Robin Jain, Hemant A. Patil, and T.K. Basu
Linear Regression for Prosody Prediction via Convex Optimization ..................................................................244
Ling Cen, Minghui Dong, and Paul Chan
Analyzing the Relationship between Formants and Pitch for Singing Voice ......................................................248
Hwee Teng Tan and Minghui Dong
Linguistic Resources and Tools
Using HTML Tags to Improve Parallel Resources Extraction ............................................................................255
Yanhui Feng, Yu Hong, Wei Tang, Jianmin Yao, and Qiaoming Zhu
Automatic Labeling and Phonetic Assessment for an Unknown Asian Language:
The Case of the “Mo Piu” North Vietnamese Minority (early results) ...............................................................260
Geneviève Caelen-Haumont, Sethserey Sam, and Eric Castelli
Automatic Construction of Chinese-Mongolian Parallel Corpora from the Web
Based on the New Heuristic Information ............................................................................................................264
Zede Zhu, Miao Li, Lei Chen, and Shouguo Zheng
Developing Bengali Speech Corpus for Phone Recognizer Using Optimum Text
Selection Technique .............................................................................................................................................268
Sandipan Mandal, Biswajit Das, Pabitra Mitra, and Anupam Basu
Polarity Shifting: Corpus Construction and Analysis ..........................................................................................272
Xiaoqian Zhang, Shoushan Li, Guodong Zhou, and Hongxia Zhao
Building a Rule-Based Malay Text Segmentation Tool ......................................................................................276
Bali Ranaivo-Malançon
Language Learning
The Phoneme-Level Articulator Dynamics for Pronunciation Animation ..........................................................283
Sheng Li, Lan Wang, and En Qi
The Effect of Arabic Language on Reading English for Arab EFL Learners: An
Eye Tracking Study .............................................................................................................................................287
Kholod S. Al-Khalifah and Hend S. Al-Khalifa
A Study on Eye Movement of Thai Students Reading Chinese Texts with
or without Marks for Word Boundaries ...............................................................................................................291
Yumei Jiao and Peng Yu
Games for Academic Vocabulary Learning through a Virtual Environment ......................................................295
Kiran Pala, Anil Kumar Singh, and Suryakanth V. Gangashetty
Author Index ......................................................................................................................................................299
ix
WordNet Editor to Refine Indonesian Language Lexical Database
Gunawan*,**), Jessica Felani Wijoyo**), I Ketut Eddy Purnama*), Mochamad Hariadi*)
*) Department of Electrical Engineering
Faculty of Industrial Technology
Institut Teknologi Sepuluh November
Surabaya, East Java, Indonesia
**) Department of Computer Science
Sekolah Tinggi Teknik Surabaya
Surabaya, East Java, Indonesia
gunawan@stts.edu, jessicafelaniwijoyo@gmail.com, ketut@ee_its.ac.id, mochar@ee_its.ac.id
Abstract—This paper describes an approach for editing
Indonesian Language Lexical Database especially noun
category and its relations. The purpose of this editor is to
refine Indonesian Lexical Database that was developed in
our previous researches. The visualization of the editor is
using graph library with some modifications and additions.
Furthermore, this editor will be web based so that everyone
can participate to improve Indonesian Language Lexical
Database. There is an administrator role that had to accept
or reject any suggestion for the changes suggested by any
member. We believe that this editing approach can also be
used to improve WordNet developed in other languages.
Keywords-editor; visualization; Indonesian Language;
lexical database; WordNet
I.
INTRODUCTION
WordNet [1] is an English lexical database developed
by Princeton University. The smallest unit in WordNet is
synonym set (synset) that has a meaning/sense. This
lexical database has two kinds of relations, those are
semantic relation between synsets and lexical relation
between words. One example of semantic relations is
hypernymy-hyponymy relation, whereas one example of
lexical relations is antonymy. Indonesian WordNet or
Indonesian Lexical Database will have the same structure
as English WordNet. In this paper, we will use Indonesian
WordNet term instead of Indonesian Lexical Database.
Indonesian WordNet has been developed in our
previous researches. This WordNet contains synset,
gloss/sense,
hypernymy-hyponymy
relation,
and
holonymy-meronymy relation. The Synset was built using
monolingual lexical resources [2], hypernymy-hyponymy
relation was built for noun category [3], gloss extraction
from web to append definition from Kamus Besar Bahasa
Indonesia (KBBI) [4], and the last is holonymy-meronymy
relation that was built for noun category. All of them are
done automatically so that sometimes they are incomplete
and still contain mistakes. Therefore, it is necessary to
build an editor for this Indonesian WordNet.
The rest of this paper is organized as follows. Section 2
is an explanation about Indonesian WordNet used in this
editor. Section 3 presents the editing approach to refine
Indonesian WordNet. Section 4 discusses about the result
of the editing that has been done. Finally, section 5 and 6
give some conclusions and further research.
II.
INDONESIAN WORDNET
In this section, the detailed information about
Indonesian WordNet with some examples of its mistakes
are presented. Each example will be equipped with the
reason why it is considered wrong.
Table 1 presents the detailed data of Indonesian
WordNet. This data are obtained from our previous
researches as described in section I. It shows that there are
69830 synsets with each gloss, 9427 pairs hypernymyhyponymy relations, and 2489 pairs holonymy-meronymy
relations .
TABLE I.
INDONESIAN WORDNET DATA
Data
Synset
Gloss
Hypernymy-Hyponymy
Holonymy-Meronymy
Amount
69830
69830
9427
2489
The 2489 pairs holonymy-meronymy relations are
categorized into 3 types, those are part, substance, and
member. Detailed of those types are described as follows.
1. If constituent object has different function, shape,
and position then it is a part. For example, kaki
(foot) is a part meronym for tubuh (body).
2. If constituent object is a substance (the content of
other object) then it is a substance. For example,
oksigen (oxygen) is a substance meronym for
udara (air).
3. If constituent object has same function then it is a
member. For example, raja (king) is a member
meronym for istana (palace).
Table 2 shows the detailed data for each type of those
relations according to the total number of holonymymeronymy relations in table 1.
TABLE II.
THREE CATEGORIES OF HOLONYMY-MERONYMY
RELATIONS
Category
Part
Substance
Member
Amount
2101
179
209
Here are some examples of wrong data. Figure 1 is an
example of wrong gloss where definition of gigi (tooth) is
pengganti akar gigi tiruan dan umumnya terbuat dari
Titanium atau paduan Titanium (artificial tooth root
replacement and usually made of Titanium or Titanium
composite). Actually, gigi (tooth) has definition tulang
keras dan kecil-kecil berwarna putih yang tumbuh
tersusun berakar di dalam gusi dan kegunaannya untuk
mengunyah atau menggigit (hard bone and various small
things composed grow rooted in the gum and used for
chewing or biting).
Wrong Gloss
gigi
tooth
pengganti akar gigi tiruan dan umumnya
terbuat dari Titanium atau paduan Titanium
artificial tooth root replacement and usually
made of Titanium or Titanium composite
If we look at some examples of wrong data in
Indonesian WordNet then it can be known that automatic
data extraction will usually have imperfect data. Therefore,
we need a manual correction using an editor.
After analyzing Indonesian WordNet data, there are
5924 wrong hypernymy-hyponymy relations and 1099
holonymy-meronymy relations to be deleted. After
deleting those relations, the hypernymy-hyponymy
relations got are 9427 - 5924 = 3503 relations and total
holonymy-meronymy relations are 2489 - 1099 = 1390
relations. Besides those deletions, there are also additions
for those relations to complete them. Detailed additions for
hypernymy-hyponymy relations and holonymy-meronymy
relations are 349 and 77 relations. The final Indonesian
WordNet data can be seen in final column in table 3.
III.
Figure 1. Example of Wrong Gloss or Definition
An example of wrong hyponymy-hypernymy relation
in Indonesian WordNet can be seen in figure 2. Synset
{las} (which is equal to {weld} in English) has synset
{herba, tumbuhan_terna} (which is equal to {herb, herb
plant} in English) as a hypernym. {Weld} is an action
while {herb, herb plant} is a kind of plant so that it is not
correct that action can have a kind of plant as its
hypernym. Because of that, this relation will be deleted.
Wrong Hypernymy-Hyponymy Relation
Hyponym :
las
penyambungan (besi dan sebagainya)
dengan cara membakar;
weld
grafting (iron, etc) by burning
Hypernym :
herba, tumbuhan_terna
tumbuhan terna
herb, herb plant
herb plant
Figure 2. Example of Wrong Hypernymy-Hyponymy Relation
Another example is a wrong holonymy-meronymy
relation (figure 3) which is gulai (curry) has duri (thorn) as
its part. Gulai (curry) is one of Indonesian cuisine that
does not have duri (thorn) as its part.
Wrong Holonymy-Meronymy Relation
Part Holonym :
gulai
sayur berkuah santan dan diberi kunyit serta
bumbu khusus (biasanya dicampur dengan ikan,
daging kambing, daging sapi, dan sebagainya);
salah satu masakan tradisional Indonesia yang
berasal dari Sumatera Barat dan terkenal
dengan kelezatannya karena mengandung
berbagai macam rempah-rempah sebagai
penyusun bumbu
curry
vegetable with coconut milk sauce and will be
given turmeric along with special seasoning
(usually mixed with fish, mutton, etc.); one of
Indonesian traditional cuisine comes from
Sumatera Barat and popular with its delicious
taste because it contains various spices as flavor
constituents
Part Meronym :
duri
gloss
thorn
gloss
Figure 3. Example of Wrong Holonymy-Meronymy Relation
EDITING APPROACH
This editing approach uses a graph visualization to
visualize Indonesian WordNet data. The purpose of this
editor is not only to correct wrong Indonesian WordNet
data but also to append it with antonymy relations for
noun category. The graph drawing method used in this
editor is Force Directed Graph so that it can produce
graph with a good aesthetic. All suggestions correction
data from member will be managed by a website
administrator. Besides that, administrator has a
responsibility to form a lexical database using grinder in
Linux Operating System.
A. Indonesian WordNet Database Structure
Structure of Indonesian WordNet database can be seen
in figure 4. Wn_gloss is a table for all senses/definitions
of synsets, wn_synset is a table for all synsets existing in
Indonesian Language, and wn_all is a table for all
relations whether semantic or lexical relations. WordNet
in languages other than Indonesian Language can also be
edited using this editor if their database are converted into
a structure like figure 4.
wn_gloss
synset_id
gloss
word
lexnames
lexid
head
wn_synset
synset_id
wnum
word
ss_type
wn_all
synset_id_1
wnum_1
synset_id_2
wnum_2
pointer
Figure 4. Indonesian WordNet Database Structure
B. Preprocessing
Before the editor uses Indonesian WordNet as input
data, there are some preprocessing steps needed. This is
because Indonesian WordNet was obtained automatically
in our previous researches as explained before. The
preprocessing steps are listed below.
1. Remove all special symbols in synset and gloss
2. Remove all tags (< >) and “gloss” keywords
3. Remove twin words
4. Replace abbreviation using word in KBBI [4]
Three examples of special symbols in synset and gloss are
ã, ©, and /. One of abbreviation in KBBI is dsb (etc.)
which stands for dan sebagainya (etcetera). After
preprocessing Indonesian WordNet, we get total 69827
synsets with each gloss and no change in the number of
relations.
C. Force Directed Graph
Force Directed Graph method [5] is one of graph
drawing method that produces a graph with as few as
crossing edges as possible. Graph created using this
method is simulated as if it were physical system by
assigning attractive and repulsive forces between nodes.
This process is done continuously to find the optimal
layout by minimizing the energy of the system. Most
WordNet data visualization uses this method, synonym [6]
for example. In this paper, the implementation of this
method uses springy and springyui libraries. Those two
libraries are used because the number of synonym words in
Indonesian WordNet synsets are varied and sometimes too
many.
D. Visualization and Editing
Editor web page explained here has two main areas,
those are editing menus located at the left (area 1) and
graph drawing canvas (area 2). Figure 5 shows how the
design of this editor web page. Editing menus consist of
word searching, graph setting, colors description, node
description, and graph changing. Graph changing menu
will be only available if members are in editing mode (by
pressing “change” button in the change graph option in the
word searching menu).
Indonesian WordNet data in this editor will be
presented in graph elements as follows. Sense and word in
each synset will be represented in node form, while all
relations whether semantic or lexical relations will be
represented in edge form. A sense is represented by its
synset number, whereas a word in synset is represented by
the word itself. Each WordNet category (noun, verb,
adjective, adverb) and relation will have different colors
which can be managed using graph setting menu.
Description of those colors can be seen in colors
description menu in figure 7.
Figure 7. Colors Description Menu
An example of WordNet data visualization (using
colors setting in figure 7) can be seen in figure 8. Node
with green color is a sense with noun category. Edge with
orange color is a synonym relation. Manusia (human
being) has 5 different noun senses/definitions/meanings in
Indonesian WordNet. In figure 8, manusia (human being)
and penunggu dunia (world watchman) are synonym in
sense number 40927.
area 1
Hyponym to Hypernym
area 2
Word
Figure 5. Editor Web Page
In graph changing menu (figure 6a), there are 4 types
of data (sense, word in synset, semantic relation, and
lexical relation) that can be managed and option to save the
suggestion data to server database or not. If administrator
agrees with that suggestion then the changes will be
applied to Indonesian WordNet and vice versa. Figure 6b
is the design of the form to add a new sense.
Synonym
Sense
Figure 8. The Visualization of Manusia (Human Being)
(a)
(b)
Figure 6. Graph Changing Menu (a) and Add New Sense Form (b)
Several functions and methods are added to the springy
and springyui libraries and also some modifications are
made to those two libraries so that they can meet the needs
of this editor. In this paper, only some of the additional
functions and methods are shown. These additional
functions and methods can be seen in figure 9.
modify sense
modify all the categories of words in the synset
modify a word in the synset
delete all lexical relations
delete all words in the synset
delete a certain word in the synset
get a node by its label
add new or delete existing semantic relation
add new or delete existing lexical relation
add new sense to existing word
get the status of the graph
get the total number of nodes
add new edge with certain type to the graph
create new node with certain type
create new edge with certain type
Figure 9. Some Additional Functions and Methods
E. Editor Website Management and Lexical Database
Formation
This editor system needs an administrator role to
manage all suggestions suggested by members. Every
suggestion that is accepted will change Indonesian
WordNet data and vice versa.
Before converting the database to a lexical database
model, the database will be converted into lexicographer
files. In this editor we use lexicographer file generator that
was created in our previous research. Afterward,
Indonesian WordNet will be created using grinder in Linux
Operating System by administrator (local/not web based).
IV.
RESULT
In this section, an explanation about the result that we
get after editing Indonesian WordNet data is given. Table
3 shows information about the initial state and the final
state. Initial state is state after preprocessing Indonesian
WordNet data, whereas final state is the final state after
correcting the data. Actually, between these two states
there are some editing phases with more than 85 volunteers
in correcting this Indonesian WordNet data.
TABLE III.
INDONESIAN WORDNET RESULT
Data
Initial
Gloss
69827
Synset
69827
Antonymy
0
Hypernymy9427
Hyponymy
Holonymy-Meronymy
Part
2101
Substance
179
Member
209
Final
69827
69827
78
3852
1141
146
180
We also compare the statistic of Indonesian WordNet
to English WordNet. Table 4 shows the comparison of
words and synsets for both languages. Both languages
have most definitions in noun category. We can see here
that Indonesian WordNet is more compact than English
WordNet. It can be seen from the total number of words
and synsets for Indonesian WordNet which are fewer than
English WordNet.
TABLE IV.
Category
Noun
Verb
Adjective
Adverb
Total
INDONESIAN AND ENGLISH WORDNET STATISTICS
Indonesian
WordNet
Words Synsets
53529
41272
30211
20798
12375
7104
1455
653
97570
69827
V.
English
WordNet
Words
Synsets
117798
82115
11529
13767
21479
18156
4481
3621
155287
117659
CONCLUSIONS
Indonesian WordNet correction needs a fairly large
number of Volunteers. This is due to the amount of
Indonesian WordNet data that is quite a lot as explained
before. Besides that, knowledge about WordNet as a
lexical database that consists of 4 categories (noun, verb,
adjective, and adverb) with semantic and lexical relations
is extremely important. This is due at the time of
correcting data by volunteers, many of them are still not
familiar with WordNet and confused with the purpose of
each relationship and term.
Visualization and editing Indonesian WordNet data
visually make user enjoy the editing process.
Representation of WordNet data in a text based can not
attract users in the process of correcting data. This editor
also shows only one relation at a time while visualizing
Indonesian WordNet data to make it clear and
understandable.
The most important thing is this editing approach, as
already described before, is probably applicable to all
WordNet with other languages than Indonesian Language.
Using this editing approach, WordNet developed in other
languages can also be refined.
VI.
FURTHER RESEARCH
Based on this editor, we find an enhancement that can
be done. The enchancement is to improve the editor so that
it can handle other Indonesian WordNet categories with
their relations besides noun category.
ACKNOWLEDGMENT
We would like to thank STTS students that had already
become volunteers to correct Indonesian WordNet data
using this editor. We also hope that this Indonesian
Wordnet can be helpful for developing Indonesian Natural
Language Processing, Text Mining, and Web Mining
applications.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
Christiane Fellbaum, “WordNet: An Electronic Lexical Database”,
Cambridge, MA: MIT Press, 1998.
Gunawan and Andy Saputra, “Building Synsets for Indonesian
WordNet with Monolingual Lexical Resources”, Proc. IALP
conference, Harbin, China, 2010.
Gunawan and Erick Pranata, “Acquisition of HypernymyHyponymy Relation between Nouns for WordNet Building”, Proc.
IALP conference, Harbin, China, 2010.
Tim Pusat Bahasa, Kamus Bahasa Indonesia, Jakarta : Pusat
Bahasa Departemen Pendidikan Nasional, 2008.
Yi Fan Hu, “Efficient and High Quality Force-Directed Graph
Drawing”, The Mathematica Journal, 10 (37-71), 2005.
WordNet
Visualization
using
Force-Directed
Graphs,
http://code.google.com/p/synonym.
on Asian Language Processing
IALP 2011
Table of Contents
Message from the General Chair ..........................................................................................................................x
Message from the Program Chairs......................................................................................................................xi
Message from the Local Organizing Chair........................................................................................................xii
Conference Committees......................................................................................................................................xiii
Program Committee............................................................................................................................................xiv
Invited Talks.........................................................................................................................................................xvi
Phonology, Morphology, Syntax, and Language Model
A Simplified-Traditional Chinese Character Conversion Model Based
on Log-Linear Models .............................................................................................................................................3
Yidong Chen, Xiaodong Shi, and Changle Zhou
Improving Chinese Dependency Parsing with Self-Disambiguating Patterns ........................................................7
Likun Qiu, Lei Wu, Kai Zhao, Changjian Hu, and Lingpeng Kong
Joint Decoding for Chinese Word Segmentation and POS Tagging Using
Character-Based and Word-Based Discriminative Models ...................................................................................11
Xinxin Li, Xuan Wang, and Lin Yao
Natural Language Grammar Induction of Indonesian Language Corpora Using
Genetic Algorithm .................................................................................................................................................15
Arya Tandy Hermawan, Gunawan, and Joan Santoso
Error-Driven Adaptive Language Modeling for Chinese Pinyin-to-Character
Conversion .............................................................................................................................................................19
Jin Hu Huang and David Powers
Theoretical Framework of Mongolian Word Segmentation Specification
for Information Processing ....................................................................................................................................23
Tong Laga and Xiaobing Zhao
Research on the Uyghur Information Database for Information Processing .........................................................26
Yusup Ebeydulla, Hesenjan Abliz, and Azragul Yusup
Sentence Boundary Detection in Colloquial Arabic Text: A Preliminary Result .................................................30
Afnan A. Al-Subaihin, Hend S. Al-Khalifa, and AbdulMalik S. Al-Salman
A Study of the Classification and Arrangement Rule of Uygur Morphemes
for Information Processing ....................................................................................................................................33
Pu Li and Shuzhen Shi
v
Graph-Based Language Model of Long-Distance Dependency ............................................................................37
Faguo Zhou and Xingang Yu
BASRAH: Arabic Verses Meters Identification System .......................................................................................41
Zainab A. Khalaf, Maytham Alabbas, and Tien-Ping Tan
Semantics
WordNet Editor to Refine Indonesian Language Lexical Database ......................................................................47
Gunawan, Jessica Felani Wijoyo, I. Ketut Eddy Purnama, and Mochamad Hariadi
Two Ontological Approaches to Building an Intergrated Semantic Network
for Yami ka-Verbs .................................................................................................................................................51
Meng-Chien Yang, Si-Wei Huang, and D. Victoria Rau
Issues with the Unergative/Unaccusative Classification of the Intransitive Verbs ...............................................55
Nitesh Surtani, Khushboo Jha, and Soma Paul
A Sentence-Level Semantic Annotated Corpus Based on HNC Theory ...............................................................59
Zhiying Liu, Yaohong Jin, and Chuanjiang Miao
An Exploration on the Modes of Word Meaning Extension Based on Metaphorical
and Metonymic Mechanisms .................................................................................................................................63
Xiaofang Ouyang
On the Semantic Orientation and Computer Identification of the Chinese Adverb
cai ...........................................................................................................................................................................67
Lin He and Pengbing Chen
Exploring Both Flat and Structured Features for Number Type Identification
of Chinese Personal Noun Phrases ........................................................................................................................71
Jun Lang
Discourse
The Context Imperative Sentences of Modern Chinese ........................................................................................77
Hao Zhao and Kaihong Yang
Research on Cross-Document Coreference of Chinese Person Name ...................................................................81
Ji Ni, Fang Kong, Peifeng Li, and Qiaoming Zhu
Research of Event Pronoun Resolution .................................................................................................................85
Ning Zhang, Fang Kong, and Peifeng Li
Co-reference Resolution in Vietnamese Documents Based on Support Vector
Machines ................................................................................................................................................................89
Duc-Trong Le, Mai-Vu Tran, Tri-Thanh Nguyen, and Quang-Thuy Ha
Research of Noun Phrase Coreference Resolution ................................................................................................93
Junwei Gao, Fang Kong, Peifeng Li, and Qiaoming Zhu
Discourse Structures of English Exposition ..........................................................................................................97
Donghong Liu and Meizhen Liao
Linguistic Competency Model for Intentional Agent ..........................................................................................101
Sivakumar Ramakrishnan and Vasuky Mohanan
vi
Text Understanding and Retrieval
The Comparison of Chinese Spam Filter Based on Generative Model
and Discriminative Model ...................................................................................................................................107
Yong Han, Yingying Wang, Huafu Ding, and Haoliang Qi
Applying Grapheme, Word, and Syllable Information for Language Identification
in Code Switching Sentences ...............................................................................................................................111
Yin-Lai Yeong and Tien-Ping Tan
An Integrated Approach Using Conditional Random Fields for Named Entity
Recognition and Person Property Extraction in Vietnamese Text .......................................................................115
Hoang-Quynh Le, Mai-Vu Tran, Nhat-Nam Bui, Nguyen-Cuong Phan,
and Quang-Thuy Ha
A Query Reformulation Model Using Markov Graphic Method ........................................................................119
Jiali Zuo and Mingwen Wang
Search Results Clustering Based on a Linear Weighting Method of Similarity ..................................................123
Dequan Zheng, Haibo Liu, and Tiejun Zhao
Extracting Pseudo-Labeled Samples for Sentiment Classification Using Emotion
Keywords .............................................................................................................................................................127
Sophia Yat Mei Lee, Daming Dai, Shoushan Li, and Kathleen Ahrens
Imbalanced Sentiment Classification with Multi-strategy Ensemble Learning ..................................................131
Zhongqing Wang, Shoushan Li, Guodong Zhou, Peifeng Li, and Qiaoming Zhu
Formalization and Rules for Recognition of Satirical Irony ................................................................................135
Lingpeng Kong and Likun Qiu
Summarization
An Automatic Linguistics Approach for Persian Document Summarization ......................................................141
Hossein Kamyar, Mohsen Kahani, Mohsen Kamyar, and Asef Poormasoomi
Context-Based Persian Multi-document Summarization (Global View) ............................................................145
Asef Poormasoomi, Mohsen Kahani, Saeed Varasteh Yazdi, and Hossein Kamyar
Centroid Integer Selection Model—A High Efficiency Method on Dynamic
Multi-document Summarization ..........................................................................................................................150
Meiling Liu, Dequan Zheng, Tiejun Zhao, and Yang Yu
Corpus Based Extractive Document Summarization for Indic Script .................................................................154
P. Vijayapal Reddy, B. Vishnu Vardhan, and A. Govardhan
Research on Multi-document Summarization Model Based on Dynamic
Manifold-Ranking ................................................................................................................................................158
Meiling Liu, Honge Ren, Dequan Zheng, and Tiejun Zhao
Machine Translation
An Orientation Model for Hierarchical Phrase-Based Translation ......................................................................165
Xinyan Xiao, Jinsong Su, Yang Liu, Qun Liu, and Shouxun Lin
vii
Improving Bilingual Lexicon Construction from Chinese-English Comparable
Corpora via Dependency Relationship Mapping .................................................................................................169
Hua Xu, Dandan Liu, Longhua Qian, and Guodong Zhou
A Rule-Based Source-Side Reordering on Phrase Structure Subtrees ................................................................173
Fangli Liang, Lei Chen, Miao Li, and Nasun-urtu
Automatic Acquisition of Chinese-Tibetan Multi-word Equivalent Pair
from Bilingual Corpora ........................................................................................................................................177
Minghua Nuo, Huidan Liu, Longlong Ma, Jian Wu, and Zhiming Ding
Character-Level System Combination: An Empirical Study for English-to-Chinese
Spoken Language Translation .............................................................................................................................181
Jinhua Du
Mining Parallel Data from Comparable Corpora via Triangulation ....................................................................185
Thi-Ngoc-Diep Do, Eric Castelli, and Laurent Besacier
Using Rich Linguistic and Contextual Information for Tree-Based Statistical
Machine Translation ............................................................................................................................................189
Bui Thanh Hung, Nguyen Le Minh, and Akira Shimazu
Research on Element Sub-sentence in Chinese-English Patent Machine Translation ........................................193
Zhiying Liu, Yaohong Jin, and Yuhuan Chi
Lexical Word Similarity for Re-ranking in Vietnamese-English Named Entity Back
Transliteration ......................................................................................................................................................197
Thi Hoang Diem Le and Ai Ti Aw
The Chinese-English Bilingual Sentence Alignment Based on Length ..............................................................201
Huafu Ding, Lili Quan, and Haoliang Qi
Optimal Translation Boundaries for BTG-Based Decoding ................................................................................205
Xiangyu Duan and Min Zhang
Spoken Language Processing
Acoustic Space in Motor Disorders of Speech: Two Case Studies .....................................................................211
Vaishna Narang, Deepshikha Misra, and Garima Dalal
Adopting Malay Syllable Structure for Syllable Based Speech Synthesizer for Iban
and Bidayuh Languages .......................................................................................................................................216
Sarah F. S. Juan, Vyonne Edwin, Chai Yeen Cheong, Jun Choi Lee, and Alvin W. Yeo
How Vietnamese Attitudes can be Recognized and Confused: Cross-Cultural
Perception and Speech Prosody Analysis ............................................................................................................220
Dang-Khoa Mac, Eric Castelli, Véronique Aubergé, and Albert Rilliard
Non-native Accent Pronunciation Modeling in Automatic Speech Recognition ................................................224
Basem H.A. Ahmed and Tien-Ping Tan
An In-car Chinese Noise Corpus for Speech Recognition ..................................................................................228
Jue Hou, Yi Liu, Chao Zhang, and Shilei Huang
An Experimental Study on Vietnamese Speech Synthesis ..................................................................................232
Liping Kui, Jian Yang, Bin He, and Enxing Hu
viii
Development of Acoustic Space in 3 to 5 Years Old Hindi Speaking Children .................................................236
Vaishna Narang, Garima Dalal, and Deepshikha Misra
Design of a Query-by-Humming System for Hindi Songs Using DDTW Based
Approach ..............................................................................................................................................................240
Prakhar K. Jain, Robin Jain, Hemant A. Patil, and T.K. Basu
Linear Regression for Prosody Prediction via Convex Optimization ..................................................................244
Ling Cen, Minghui Dong, and Paul Chan
Analyzing the Relationship between Formants and Pitch for Singing Voice ......................................................248
Hwee Teng Tan and Minghui Dong
Linguistic Resources and Tools
Using HTML Tags to Improve Parallel Resources Extraction ............................................................................255
Yanhui Feng, Yu Hong, Wei Tang, Jianmin Yao, and Qiaoming Zhu
Automatic Labeling and Phonetic Assessment for an Unknown Asian Language:
The Case of the “Mo Piu” North Vietnamese Minority (early results) ...............................................................260
Geneviève Caelen-Haumont, Sethserey Sam, and Eric Castelli
Automatic Construction of Chinese-Mongolian Parallel Corpora from the Web
Based on the New Heuristic Information ............................................................................................................264
Zede Zhu, Miao Li, Lei Chen, and Shouguo Zheng
Developing Bengali Speech Corpus for Phone Recognizer Using Optimum Text
Selection Technique .............................................................................................................................................268
Sandipan Mandal, Biswajit Das, Pabitra Mitra, and Anupam Basu
Polarity Shifting: Corpus Construction and Analysis ..........................................................................................272
Xiaoqian Zhang, Shoushan Li, Guodong Zhou, and Hongxia Zhao
Building a Rule-Based Malay Text Segmentation Tool ......................................................................................276
Bali Ranaivo-Malançon
Language Learning
The Phoneme-Level Articulator Dynamics for Pronunciation Animation ..........................................................283
Sheng Li, Lan Wang, and En Qi
The Effect of Arabic Language on Reading English for Arab EFL Learners: An
Eye Tracking Study .............................................................................................................................................287
Kholod S. Al-Khalifah and Hend S. Al-Khalifa
A Study on Eye Movement of Thai Students Reading Chinese Texts with
or without Marks for Word Boundaries ...............................................................................................................291
Yumei Jiao and Peng Yu
Games for Academic Vocabulary Learning through a Virtual Environment ......................................................295
Kiran Pala, Anil Kumar Singh, and Suryakanth V. Gangashetty
Author Index ......................................................................................................................................................299
ix
WordNet Editor to Refine Indonesian Language Lexical Database
Gunawan*,**), Jessica Felani Wijoyo**), I Ketut Eddy Purnama*), Mochamad Hariadi*)
*) Department of Electrical Engineering
Faculty of Industrial Technology
Institut Teknologi Sepuluh November
Surabaya, East Java, Indonesia
**) Department of Computer Science
Sekolah Tinggi Teknik Surabaya
Surabaya, East Java, Indonesia
gunawan@stts.edu, jessicafelaniwijoyo@gmail.com, ketut@ee_its.ac.id, mochar@ee_its.ac.id
Abstract—This paper describes an approach for editing
Indonesian Language Lexical Database especially noun
category and its relations. The purpose of this editor is to
refine Indonesian Lexical Database that was developed in
our previous researches. The visualization of the editor is
using graph library with some modifications and additions.
Furthermore, this editor will be web based so that everyone
can participate to improve Indonesian Language Lexical
Database. There is an administrator role that had to accept
or reject any suggestion for the changes suggested by any
member. We believe that this editing approach can also be
used to improve WordNet developed in other languages.
Keywords-editor; visualization; Indonesian Language;
lexical database; WordNet
I.
INTRODUCTION
WordNet [1] is an English lexical database developed
by Princeton University. The smallest unit in WordNet is
synonym set (synset) that has a meaning/sense. This
lexical database has two kinds of relations, those are
semantic relation between synsets and lexical relation
between words. One example of semantic relations is
hypernymy-hyponymy relation, whereas one example of
lexical relations is antonymy. Indonesian WordNet or
Indonesian Lexical Database will have the same structure
as English WordNet. In this paper, we will use Indonesian
WordNet term instead of Indonesian Lexical Database.
Indonesian WordNet has been developed in our
previous researches. This WordNet contains synset,
gloss/sense,
hypernymy-hyponymy
relation,
and
holonymy-meronymy relation. The Synset was built using
monolingual lexical resources [2], hypernymy-hyponymy
relation was built for noun category [3], gloss extraction
from web to append definition from Kamus Besar Bahasa
Indonesia (KBBI) [4], and the last is holonymy-meronymy
relation that was built for noun category. All of them are
done automatically so that sometimes they are incomplete
and still contain mistakes. Therefore, it is necessary to
build an editor for this Indonesian WordNet.
The rest of this paper is organized as follows. Section 2
is an explanation about Indonesian WordNet used in this
editor. Section 3 presents the editing approach to refine
Indonesian WordNet. Section 4 discusses about the result
of the editing that has been done. Finally, section 5 and 6
give some conclusions and further research.
II.
INDONESIAN WORDNET
In this section, the detailed information about
Indonesian WordNet with some examples of its mistakes
are presented. Each example will be equipped with the
reason why it is considered wrong.
Table 1 presents the detailed data of Indonesian
WordNet. This data are obtained from our previous
researches as described in section I. It shows that there are
69830 synsets with each gloss, 9427 pairs hypernymyhyponymy relations, and 2489 pairs holonymy-meronymy
relations .
TABLE I.
INDONESIAN WORDNET DATA
Data
Synset
Gloss
Hypernymy-Hyponymy
Holonymy-Meronymy
Amount
69830
69830
9427
2489
The 2489 pairs holonymy-meronymy relations are
categorized into 3 types, those are part, substance, and
member. Detailed of those types are described as follows.
1. If constituent object has different function, shape,
and position then it is a part. For example, kaki
(foot) is a part meronym for tubuh (body).
2. If constituent object is a substance (the content of
other object) then it is a substance. For example,
oksigen (oxygen) is a substance meronym for
udara (air).
3. If constituent object has same function then it is a
member. For example, raja (king) is a member
meronym for istana (palace).
Table 2 shows the detailed data for each type of those
relations according to the total number of holonymymeronymy relations in table 1.
TABLE II.
THREE CATEGORIES OF HOLONYMY-MERONYMY
RELATIONS
Category
Part
Substance
Member
Amount
2101
179
209
Here are some examples of wrong data. Figure 1 is an
example of wrong gloss where definition of gigi (tooth) is
pengganti akar gigi tiruan dan umumnya terbuat dari
Titanium atau paduan Titanium (artificial tooth root
replacement and usually made of Titanium or Titanium
composite). Actually, gigi (tooth) has definition tulang
keras dan kecil-kecil berwarna putih yang tumbuh
tersusun berakar di dalam gusi dan kegunaannya untuk
mengunyah atau menggigit (hard bone and various small
things composed grow rooted in the gum and used for
chewing or biting).
Wrong Gloss
gigi
tooth
pengganti akar gigi tiruan dan umumnya
terbuat dari Titanium atau paduan Titanium
artificial tooth root replacement and usually
made of Titanium or Titanium composite
If we look at some examples of wrong data in
Indonesian WordNet then it can be known that automatic
data extraction will usually have imperfect data. Therefore,
we need a manual correction using an editor.
After analyzing Indonesian WordNet data, there are
5924 wrong hypernymy-hyponymy relations and 1099
holonymy-meronymy relations to be deleted. After
deleting those relations, the hypernymy-hyponymy
relations got are 9427 - 5924 = 3503 relations and total
holonymy-meronymy relations are 2489 - 1099 = 1390
relations. Besides those deletions, there are also additions
for those relations to complete them. Detailed additions for
hypernymy-hyponymy relations and holonymy-meronymy
relations are 349 and 77 relations. The final Indonesian
WordNet data can be seen in final column in table 3.
III.
Figure 1. Example of Wrong Gloss or Definition
An example of wrong hyponymy-hypernymy relation
in Indonesian WordNet can be seen in figure 2. Synset
{las} (which is equal to {weld} in English) has synset
{herba, tumbuhan_terna} (which is equal to {herb, herb
plant} in English) as a hypernym. {Weld} is an action
while {herb, herb plant} is a kind of plant so that it is not
correct that action can have a kind of plant as its
hypernym. Because of that, this relation will be deleted.
Wrong Hypernymy-Hyponymy Relation
Hyponym :
las
penyambungan (besi dan sebagainya)
dengan cara membakar;
weld
grafting (iron, etc) by burning
Hypernym :
herba, tumbuhan_terna
tumbuhan terna
herb, herb plant
herb plant
Figure 2. Example of Wrong Hypernymy-Hyponymy Relation
Another example is a wrong holonymy-meronymy
relation (figure 3) which is gulai (curry) has duri (thorn) as
its part. Gulai (curry) is one of Indonesian cuisine that
does not have duri (thorn) as its part.
Wrong Holonymy-Meronymy Relation
Part Holonym :
gulai
sayur berkuah santan dan diberi kunyit serta
bumbu khusus (biasanya dicampur dengan ikan,
daging kambing, daging sapi, dan sebagainya);
salah satu masakan tradisional Indonesia yang
berasal dari Sumatera Barat dan terkenal
dengan kelezatannya karena mengandung
berbagai macam rempah-rempah sebagai
penyusun bumbu
curry
vegetable with coconut milk sauce and will be
given turmeric along with special seasoning
(usually mixed with fish, mutton, etc.); one of
Indonesian traditional cuisine comes from
Sumatera Barat and popular with its delicious
taste because it contains various spices as flavor
constituents
Part Meronym :
duri
gloss
thorn
gloss
Figure 3. Example of Wrong Holonymy-Meronymy Relation
EDITING APPROACH
This editing approach uses a graph visualization to
visualize Indonesian WordNet data. The purpose of this
editor is not only to correct wrong Indonesian WordNet
data but also to append it with antonymy relations for
noun category. The graph drawing method used in this
editor is Force Directed Graph so that it can produce
graph with a good aesthetic. All suggestions correction
data from member will be managed by a website
administrator. Besides that, administrator has a
responsibility to form a lexical database using grinder in
Linux Operating System.
A. Indonesian WordNet Database Structure
Structure of Indonesian WordNet database can be seen
in figure 4. Wn_gloss is a table for all senses/definitions
of synsets, wn_synset is a table for all synsets existing in
Indonesian Language, and wn_all is a table for all
relations whether semantic or lexical relations. WordNet
in languages other than Indonesian Language can also be
edited using this editor if their database are converted into
a structure like figure 4.
wn_gloss
synset_id
gloss
word
lexnames
lexid
head
wn_synset
synset_id
wnum
word
ss_type
wn_all
synset_id_1
wnum_1
synset_id_2
wnum_2
pointer
Figure 4. Indonesian WordNet Database Structure
B. Preprocessing
Before the editor uses Indonesian WordNet as input
data, there are some preprocessing steps needed. This is
because Indonesian WordNet was obtained automatically
in our previous researches as explained before. The
preprocessing steps are listed below.
1. Remove all special symbols in synset and gloss
2. Remove all tags (< >) and “gloss” keywords
3. Remove twin words
4. Replace abbreviation using word in KBBI [4]
Three examples of special symbols in synset and gloss are
ã, ©, and /. One of abbreviation in KBBI is dsb (etc.)
which stands for dan sebagainya (etcetera). After
preprocessing Indonesian WordNet, we get total 69827
synsets with each gloss and no change in the number of
relations.
C. Force Directed Graph
Force Directed Graph method [5] is one of graph
drawing method that produces a graph with as few as
crossing edges as possible. Graph created using this
method is simulated as if it were physical system by
assigning attractive and repulsive forces between nodes.
This process is done continuously to find the optimal
layout by minimizing the energy of the system. Most
WordNet data visualization uses this method, synonym [6]
for example. In this paper, the implementation of this
method uses springy and springyui libraries. Those two
libraries are used because the number of synonym words in
Indonesian WordNet synsets are varied and sometimes too
many.
D. Visualization and Editing
Editor web page explained here has two main areas,
those are editing menus located at the left (area 1) and
graph drawing canvas (area 2). Figure 5 shows how the
design of this editor web page. Editing menus consist of
word searching, graph setting, colors description, node
description, and graph changing. Graph changing menu
will be only available if members are in editing mode (by
pressing “change” button in the change graph option in the
word searching menu).
Indonesian WordNet data in this editor will be
presented in graph elements as follows. Sense and word in
each synset will be represented in node form, while all
relations whether semantic or lexical relations will be
represented in edge form. A sense is represented by its
synset number, whereas a word in synset is represented by
the word itself. Each WordNet category (noun, verb,
adjective, adverb) and relation will have different colors
which can be managed using graph setting menu.
Description of those colors can be seen in colors
description menu in figure 7.
Figure 7. Colors Description Menu
An example of WordNet data visualization (using
colors setting in figure 7) can be seen in figure 8. Node
with green color is a sense with noun category. Edge with
orange color is a synonym relation. Manusia (human
being) has 5 different noun senses/definitions/meanings in
Indonesian WordNet. In figure 8, manusia (human being)
and penunggu dunia (world watchman) are synonym in
sense number 40927.
area 1
Hyponym to Hypernym
area 2
Word
Figure 5. Editor Web Page
In graph changing menu (figure 6a), there are 4 types
of data (sense, word in synset, semantic relation, and
lexical relation) that can be managed and option to save the
suggestion data to server database or not. If administrator
agrees with that suggestion then the changes will be
applied to Indonesian WordNet and vice versa. Figure 6b
is the design of the form to add a new sense.
Synonym
Sense
Figure 8. The Visualization of Manusia (Human Being)
(a)
(b)
Figure 6. Graph Changing Menu (a) and Add New Sense Form (b)
Several functions and methods are added to the springy
and springyui libraries and also some modifications are
made to those two libraries so that they can meet the needs
of this editor. In this paper, only some of the additional
functions and methods are shown. These additional
functions and methods can be seen in figure 9.
modify sense
modify all the categories of words in the synset
modify a word in the synset
delete all lexical relations
delete all words in the synset
delete a certain word in the synset
get a node by its label
add new or delete existing semantic relation
add new or delete existing lexical relation
add new sense to existing word
get the status of the graph
get the total number of nodes
add new edge with certain type to the graph
create new node with certain type
create new edge with certain type
Figure 9. Some Additional Functions and Methods
E. Editor Website Management and Lexical Database
Formation
This editor system needs an administrator role to
manage all suggestions suggested by members. Every
suggestion that is accepted will change Indonesian
WordNet data and vice versa.
Before converting the database to a lexical database
model, the database will be converted into lexicographer
files. In this editor we use lexicographer file generator that
was created in our previous research. Afterward,
Indonesian WordNet will be created using grinder in Linux
Operating System by administrator (local/not web based).
IV.
RESULT
In this section, an explanation about the result that we
get after editing Indonesian WordNet data is given. Table
3 shows information about the initial state and the final
state. Initial state is state after preprocessing Indonesian
WordNet data, whereas final state is the final state after
correcting the data. Actually, between these two states
there are some editing phases with more than 85 volunteers
in correcting this Indonesian WordNet data.
TABLE III.
INDONESIAN WORDNET RESULT
Data
Initial
Gloss
69827
Synset
69827
Antonymy
0
Hypernymy9427
Hyponymy
Holonymy-Meronymy
Part
2101
Substance
179
Member
209
Final
69827
69827
78
3852
1141
146
180
We also compare the statistic of Indonesian WordNet
to English WordNet. Table 4 shows the comparison of
words and synsets for both languages. Both languages
have most definitions in noun category. We can see here
that Indonesian WordNet is more compact than English
WordNet. It can be seen from the total number of words
and synsets for Indonesian WordNet which are fewer than
English WordNet.
TABLE IV.
Category
Noun
Verb
Adjective
Adverb
Total
INDONESIAN AND ENGLISH WORDNET STATISTICS
Indonesian
WordNet
Words Synsets
53529
41272
30211
20798
12375
7104
1455
653
97570
69827
V.
English
WordNet
Words
Synsets
117798
82115
11529
13767
21479
18156
4481
3621
155287
117659
CONCLUSIONS
Indonesian WordNet correction needs a fairly large
number of Volunteers. This is due to the amount of
Indonesian WordNet data that is quite a lot as explained
before. Besides that, knowledge about WordNet as a
lexical database that consists of 4 categories (noun, verb,
adjective, and adverb) with semantic and lexical relations
is extremely important. This is due at the time of
correcting data by volunteers, many of them are still not
familiar with WordNet and confused with the purpose of
each relationship and term.
Visualization and editing Indonesian WordNet data
visually make user enjoy the editing process.
Representation of WordNet data in a text based can not
attract users in the process of correcting data. This editor
also shows only one relation at a time while visualizing
Indonesian WordNet data to make it clear and
understandable.
The most important thing is this editing approach, as
already described before, is probably applicable to all
WordNet with other languages than Indonesian Language.
Using this editing approach, WordNet developed in other
languages can also be refined.
VI.
FURTHER RESEARCH
Based on this editor, we find an enhancement that can
be done. The enchancement is to improve the editor so that
it can handle other Indonesian WordNet categories with
their relations besides noun category.
ACKNOWLEDGMENT
We would like to thank STTS students that had already
become volunteers to correct Indonesian WordNet data
using this editor. We also hope that this Indonesian
Wordnet can be helpful for developing Indonesian Natural
Language Processing, Text Mining, and Web Mining
applications.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
Christiane Fellbaum, “WordNet: An Electronic Lexical Database”,
Cambridge, MA: MIT Press, 1998.
Gunawan and Andy Saputra, “Building Synsets for Indonesian
WordNet with Monolingual Lexical Resources”, Proc. IALP
conference, Harbin, China, 2010.
Gunawan and Erick Pranata, “Acquisition of HypernymyHyponymy Relation between Nouns for WordNet Building”, Proc.
IALP conference, Harbin, China, 2010.
Tim Pusat Bahasa, Kamus Bahasa Indonesia, Jakarta : Pusat
Bahasa Departemen Pendidikan Nasional, 2008.
Yi Fan Hu, “Efficient and High Quality Force-Directed Graph
Drawing”, The Mathematica Journal, 10 (37-71), 2005.
WordNet
Visualization
using
Force-Directed
Graphs,
http://code.google.com/p/synonym.