Table Of ContentLecture Notes in Artificial Intelligence 7614
Subseries of Lecture Notes in Computer Science
LNAISeriesEditors
RandyGoebel
UniversityofAlberta,Edmonton,Canada
YuzuruTanaka
HokkaidoUniversity,Sapporo,Japan
WolfgangWahlster
DFKIandSaarlandUniversity,Saarbrücken,Germany
LNAIFoundingSeriesEditor
JoergSiekmann
DFKIandSaarlandUniversity,Saarbrücken,Germany
Hitoshi Isahara Kyoko Kanzaki (Eds.)
Advances in
Natural Language
Processing
8thInternationalConferenceonNLP,JapTAL2012
Kanazawa, Japan, October 22-24, 2012
Proceedings
1 3
SeriesEditors
RandyGoebel,UniversityofAlberta,Edmonton,Canada
JörgSiekmann,UniversityofSaarland,Saarbrücken,Germany
WolfgangWahlster,DFKIandUniversityofSaarland,Saarbrücken,Germany
VolumeEditors
HitoshiIsahara
ToyohashiUniversityofTechnology
InformationandMediaCenter
1-1Hibarigaoka,Tenpakucho
Toyohashi441-8580,Japan
E-mail:[email protected]
KyokoKanzaki
ToyohashiUniversityofTechnology
InformationandMediaCenter
1-1Hibarigaoka,Tenpakucho
Toyohashi441-8580,Japan
E-mail:[email protected]
ISSN0302-9743 e-ISSN1611-3349
ISBN978-3-642-33982-0 e-ISBN978-3-642-33983-7
DOI10.1007/978-3-642-33983-7
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2012948107
CRSubjectClassification(1998):I.2,H.3,H.4,H.5,H.2,J.1,I.5
LNCSSublibrary:SL7–ArtificialIntelligence
©Springer-VerlagBerlinHeidelberg2012
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Preface
The 8th International Conference on Natural Language Processing (JapTAL
2012) took place in the city of Kanazawa,a leading tourist destination where 7
milliontouristsvisiteveryyear.Itwasagreatcastletownruledbyaninfluential
leaderfromtheseventeenthcenturytothesecondhalfofthenineteenthcentury.
Kanazawa has not suffered from any war devastation or big natural disaster up
tonow.Therefore,itmaintainsrowsofhistoricalhousesandinheritstraditional
handicrafts and traditional performing arts.
JapTALwastheeighthintheseriesoftheTALconferences,followingIceTAL
2010 (Reykjavik, Iceland), GoTAL 2008 (Gothenburg, Sweden), FinTAL 2006
(Turku,Finland),EsTAL2004(Alicante,Spain),PorTAL2002(Faro,Portugal),
VexTAL 1999 (Venice, Italy), and FracTAL 1997 (Besancon, France). This was
the first time that the TAL conference was held outside Europe.
Themainpurposeofthe TALconferenceseriesistobringtogetherscientists
representing linguistics, computer science, and related fields, sharing a common
interest in the advancement of computational linguistics and natural language
processing.
The conference consists of invited talks, oral and poster presentations, and
special sessions on the applications and theory of natural language processing
and related areas. It provides excellent opportunities for the presentation of in-
terestingnew researchresultsanddiscussionsaboutthem,leadingtoknowledge
transfer and the generation of new ideas.
In the reviewing process of the main conference track, we received 42 sub-
missions. Among them, we selected 27 submissions as long papers and five sub-
missions as short papers. Therefore, the acceptance ratio for the long papers is
64% and the total acceptance ratio including short papers is 76%.
We had two special sessions, i.e., “Game and NLP” and “Student/Young
ResearcherSession.”Paperssubmittedforthesesessionsweretreatedseparately
andacceptedpapers werepresentedineachsessionbut they arenotincluded in
this volume.
AsconferenceorganizersofJapTAL2012,wewouldliketothankallPCmem-
berswhoreviewedsubmissionsverytightscheduleandalllocalstaffwhohelped
us during the preparationof JapTAL. We would like to thank the International
ExchangeProgramoftheNationalInstituteofInformationandCommunications
Technology (NICT) for its support of JapTAL 2012.
Hitoshi Isahara
Kyoko Kanzaki
Organization
Program Chairs
Hitoshi Isahara
Kyoko Kanzaki
Program Committee
Takako Aikawa Microsoft Research, USA
Johan Bos University of Groningen, The Netherlands
Pierrette Bouillon Geneva University, Switzerland
Caroline Brun Xerox Corporation,France
Sylviane Cardey University of Franche-Comt´e,France
Key-Sun Choi KAIST, Korea
Christiane Fellbaum Princeton University, USA
Filip Ginter University of Turku, Finland
Peter Greenfield University of Franche-Comt´e,France
Philippe de Groote INRIA, France
Yurie Iribe ToyohashiUniversity of Technology, Japan
Katsunori Kotani Kansai Gaidai University, Japan
Krister Lind´en University of Helsinki, Finland
Hrafn Loftsson Reykjavik University, Iceland
Qing Ma Ryukoku University, Japan
Bente Maegaard University of Copenhagen, Denmark
Mathieu Morey Nanyang TechnologicalUniversity, Singapore
Masayuki Okabe ToyohashiUniversity of Technology, Japan
Guy Perrier INRIA, France
KiyoakiShirai JAIST, Japan
Virach Sornlertlamvanich NECTEC, Thailand
Koichi Takeuchi Okayama University, Japan
Midori Tatsumi ToyohashiUniversity of Technology, Japan
Izabella Thomas University of Franche-Comt´e,France
Noriko Tomuro DePaul University, USA
Masatoshi Tsuchiya ToyohashiUniversity of Technology, Japan
Jose Luis Vicedo University of Alicante, Spain
Simo Vihjanen Lingsoft Ltd., Finland
Xiaohong Wu Qinghai University for Nationalities, China
Kazuhide Yamamoto Nagaoka University of Technology, Japan
Yujie Zhang Beijing Jiaotong University, China
Tiejun Zhao Harbin Institute of Technology, China
Table of Contents
Machine Translation
The Impact of Crowdsourcing Post-editing with the Collaborative
Translation Framework ........................................... 1
Takako Aikawa, Kentaro Yamamoto, and Hitoshi Isahara
Translation of Quantifiers in Japanese-Chinese Machine Translation .... 11
Shaoyu Chen and Tadahiro Matsumoto
Toward PracticalUse of Machine Translation........................ 23
Hitoshi Isahara
Phrase-LevelPattern-Based Machine Translation Based on Analogical
Mapping Method ................................................ 28
Jun Sakata, Masato Tokuhisa, and Jin’ichi Murakami
Multilingual Issues
ParallelTexts Extraction from Multimodal Comparable Corpora....... 40
Haithem Afli, Lo¨ıc Barrault, and Holger Schwenk
A Reliable Communication System to Maximize the Communication
Quality ......................................................... 52
Gan Jin and Natallia Khatseyeva
DAnIEL: Language Independent Character-BasedNews Surveillance ... 64
Ga¨el Lejeune, Romain Brixtel, Antoine Doucet, and Nadine Lucas
OOVTermTranslation,ContextInformationandDefinitionExtraction
Based on OOV Term Type Prediction .............................. 76
Jian Qu, Akira Shimazu, and Le Minh Nguyen
Exploiting a Web-Based Encyclopedia as a Knowledge Base for the
Extraction of Multilingual Terminology ............................. 88
Fatiha Sadat
Segmenting Long Sentence Pairs to Improve Word Alignment in
English-Hindi Parallel Corpora .................................... 97
Jyoti Srivastava and Sudip Sanyal
Shallow Syntactic Preprocessing for Statistical Machine Translation .... 108
Hoai-Thu Vuong, Dao Ngoc Tu, Minh Le Nguyen, and
Vinh Van Nguyen
VIII Table of Contents
Resouces
Linguistic Rules Based Approachfor Automatic Restorationof Accents
on French Texts ................................................. 118
Paul Brillant Feuto Njonko, Sylviane Cardey-Greenfield, and
Peter Greenfield
Word Clustering for Persian Statistical Parsing ...................... 126
Masood Ghayoomi
Building a Lexically and Semantically-Rich Resource for Paraphrase
Processing ...................................................... 138
Wannachai Kampeera and Sylviane Cardey-Greenfield
Tagset Conversionwith Decision Trees.............................. 144
Bartosz Zaborowski and Adam Przepi´orkowski
Fitting a Round Peg in a Square Hole: Japanese Resource Grammar in
GF............................................................. 156
Elizaveta Zimina
Semantic Analysis
Arabic Language Analyzer with Lemma Extractionand Rich Tagset.... 168
Ahmed H. Aliwy
Tracking Researcher Mobility on the Web Using Snippet Semantic
Analysis ........................................................ 180
Jorge J. Garc´ıa Flores, Pierre Zweigenbaum, Zhao Yue, and
William Turner
Semantic Role Labelling without Deep Syntactic Parsing.............. 192
Konrad Gol(cid:2)uchowski and Adam Przepio´rkowski
Temporal Information Extraction with Cross-Language Projected
Data ........................................................... 198
Przemysl(cid:2)aw Jarzebowski and Adam Przepio´rkowski
(cid:2)
WordSense DisambiguationBasedon ExampleSentences inDictionary
and Automatically Acquired from ParallelCorpus.................... 210
Pulkit Kathuria and Kiyoaki Shirai
A Study on HierarchicalTable of Indexes for Multi-documents......... 222
Tho Thi Ngoc Le, Minh Le Nguyen, and Akira Shimazu
Finding Good Initial Cluster Center by Using Maximum Average
Distance ........................................................ 228
Samuel Sangkon Lee and Chia Y. Han
Table of Contents IX
Applying a Burst Model to Detect Bursty Topics in a Topic Model ..... 239
Yusuke Takahashi, Takehito Utsuro, Masaharu Yoshioka,
Noriko Kando, Tomohiro Fukuhara, Hiroshi Nakagawa, and
Yoji Kiyota
UDRST: A Novel System for Unlabeled Discourse Parsing in the RST
Framework...................................................... 250
Ngo Xuan Bach, Nguyen Le Minh, and Akira Shimazu
Sentiment Analysis
Long-TermGoal Discoveryin the Twitter Posts throughthe Word-Pair
LDA Model ..................................................... 262
Dandan Zhu, Yusuke Fukazawa, Eleftherios Karapetsas, and Jun Ota
Finding Social Relationships by Extracting Polite Language in
Micro-blog Exchanges ............................................ 268
Norinobu Hatamoto, Yoshiaki Kurosawa, Shogo Hamada,
Kazuya Mera, and Toshiyuki Takezawa
Twitter Sentiment Analysis Based on Writing Style .................. 278
Hiroshi Maeda, Kazutaka Shimada, and Tsutomu Endo
Extraction of User Opinions by Adjective-Context Co-clustering for
Game Review Texts .............................................. 289
Kevin Raison, Noriko Tomuro, Steve Lytinen, and Jose P. Zagal
Speech and Generation
Automatic Phone Alignment: A Comparison between Speaker-
Independent Models and Models Trained on the Corpus to
Align........................................................... 300
Sandrine Brognaux, Sophie Roekhaut, Thomas Drugman, and
Richard Beaufort
A Story Generation System Based on Propp Theory: As a Mechanism
in an Integrated Narrative Generation System ....................... 312
Shohei Imabuchi and Takashi Ogata
Automatic Utterance Generation by Keeping Track of the
Conversation’sFocus within the Utterance Window .................. 322
Yusuke Nishio and Dongli Han
Author Index.................................................. 333
The Impact of Crowdsourcing Post-editing
with the Collaborative Translation Framework
Takako Aikawa1, Kentaro Yamamoto2, and Hitoshi Isahara2
1 Microsoft Research, Machine Translation Team
[email protected]
2 Toyohashi University of Technology
[email protected], [email protected]
Abstract. This paper presents a preliminary report on the impact of
crowdsourcing post-editing through the so-called “Collaborative Translation
Framework” (CTF) developed by the Machine Translation team at Microsoft
Research. We first provide a high-level overview of CTF and explain the basic
functionalities available from CTF. Next, we provide the motivation and design
of our crowdsourcing post-editing project using CTF. Last, we present the re-
sults from the project and our observations. Crowdsourcing translation is an in-
creasingly popular-trend in the MT community, and we hope that our paper can
shed new light on the research into crowdsourcing translation.
Keywords: Crowdsourcing post-editing, Collaborative Translation Framework.
1 Introduction
The output of machine translation (MT) can be used either as-is (i.e., raw-MT) or for
post-editing (i.e., MT for post-editing). Although the advancement of MT technology
is making raw-MT use more pervasive, reservations about raw-MT still persist; espe-
cially among users who need to worry about the accuracy of the translated contents
(e.g., government organizations, education institutes, NPO/NGO, enterprises, etc.).
Professional human translation from scratch, however, is just too expensive. To re-
duce the cost of translation while achieving high translation quality, many places use
MT for post-editing; that is, use MT output as an initial draft of translation and let
human translators post-edit it. Many researchers (both from academia and industry)
have been investigating how to optimize the post-editing process and developing tools
that can achieve high productivity gains via MT for post-editing.1
Recently, another type of approach to reduce the cost of translation has surfaced;
namely, crowdsourcing translation. Crowdsourcing translation started as a method to
create training/evaluation data for statistical machine translation (SMT). For instance,
with Amazon’s Mechanical Turk, one can create a huge amount of bilingual corpus
1 See Allen (2003, 2005)[1][2], O’Brien (2005)[3], Guerberof (2009a/b)[4][5], Koehn and
Haddow (2009)[6], for instance.
H. Isahara and K. Kanzaki (Eds.): JapTAL 2012, LNAI 7614, pp. 1–10, 2012.
© Springer-Verlag Berlin Heidelberg 2012
2 T. Aikawa, K. Yamamoto, and H. Isahara
data to build a new SMT system in a relatively inexpensive and quick way (Ambati et
al. (2010)[7], Zaidan and Callison-Burch (2011)[8], Ambati and Vogel (2011)[9]).2
This paper introduces a new way of crowdsourcing translation. Our approach is
unique in that it focuses on post-editing and uses a different platform; namely, the
Collaborative Translation Framework (CTF) developed by the Machine Translation
team at Microsoft Research. For our project, we used foreign students at Toyohashi
University of Technology as editors and asked them to post-edit the MT output of the
university’s English websites (http://www.tut.ac.jp/english/introduction/) via Mi-
crosoft Translator (http://www.microsofttranslator.com) into their own languages
using the CTF functionalities. This paper is a preliminary report on the results from
this project. The organization of the paper is as follows: Section 2 provides a high
level overview of CTF while describing various functionalities associated with CTF.
Section 3 presents the design of our crowdsourcing project using Toyohashi Universi-
ty of Technology websites. Section 4 presents a preliminary report on the results from
the project and Section 5 provides our concluding remarks.
2 Collaborative Translation Framework (CTF)
As mentioned at the outset of the paper, CTF has been developed by the Machine
Translation team at Microsoft Research. CTF aims to create an environment where
MT and humans can help each other to improve translation quality in an effective
way. One of the prominent functionalities of CTF is to allow users to modify or edit
the MT output from Microsoft Translator. Thus, with CTF, we can utilize the power
of crowdsourcing to post-edit MT output. There are other types of functionalities
associated with CTF, and in the following subsections, we describe these in more
detail.
2.1 Basic Functionalities of CTF
CTF functionalities have been fully integrated into Microsoft Translator’s Widget
(http://www.microsofttranslator.com/widget), and one can experience how CTF
works by visiting any website(s) with this Widget.3 For instance, let us look at Figure
1, which is the snapshot of the Widget on the English homepage at Toyohashi Univer-
sity of Technology (http://www.tut.ac.jp/english/introduction/). With this Widget,
users (or visitors of this website) can translate the entire web site automaticallyinto
their own languages; select their target languages (in Figure 1, Japanese is being
2 Amazon’s Mechanical Turk has been also used for creating different types of data as well.
For instance, see Callison-Burch (2009) [10] and Higgins et.al (2010)[11].
3 CTF functionalities can also be called via Microsoft Translator’s public API’s. For more
details on Microsoft Translator’s API’s, visit http://msdn.microsoft.com/
en-us/library/dd576287
