Sense-Tagging Verbs in English and Chinese. Hoa Trang Dang
|
|
- Donna Tucker
- 8 years ago
- Views:
Transcription
1 Sense-Tagging Verbs in English and Chinese Hoa Trang Dang Department of Computer and Information Sciences University of Pennsylvania October 30, 2003
2 Outline English sense-tagging Senseval-1 verbs Senseval-2 verbs WordNet verb sense groupings Chinese sense-tagging Penn Chinese Treebank People s Daily News Sense-tagging in PropBank II 1
3 Local Contextual Predicates for English WSD Collocational (Ratnaparkhi pos-tagger): target verb w; pos of w; pos of words at positions -1, +1, wrt w; words at positions -2, -1, +1, +2, wrt w syntactic (Collins parser): is the sentence containing w passive; is there a sentential complement, subject, direct object, or indirect object the words (if any) in the positions of subject, direct object, indirect object, particle, prepositional complement (and its object) semantic (Nymble: Bikel et al.): Named Entity tag (PERSON, ORGANIZATION, LOCATION) for proper nouns, and WN synsets and hypernyms for all nouns in above syntactic relation to w 2
4 Topical Contextual Keywords Generate list of keywords from training set for each verb: Sort all words k by entropy È of Ë Ò µ, where k appears anywhere in context, provided that k appears in more than (= 2) instances in the corpus Select words k with lowest entropy (most informative) 3
5 Senseval-1 Lexical Sample Task Lexicon: Hector lexical database, senses are organized in hierarchies Corpus: British National Corpus High average inter-annotator agreement (95.5%) 13 verbs (12 senses/verb in corpus) Avg training set size: 215 instances/verb Baseline (most frequent sense): 57% 4
6 Senseval-1 Verb Results System Accuracy p-value Avg. System ETS (Naive Bayes) MaxEnt (lex+trans+topic) MaxEnt (best variants) JHU-final (Decision List)
7 Senseval-2 English Verb Lexical Sample Task Lexicon: WordNet1.7; senses are also grouped Corpus: Penn Treebank WSJ, supplemented with British National Corpus Inter-annotator agreement: 71% 29 verbs, mostly highly polysemous (16 senses/verb in corpus) Avg training set size: 110 instances/verb Baseline (most frequent sense): 40% Best system performance: 60% 6
8 System Accuracy and Feature Types (English) Feature (local) Accuracy Feature (local, topic) Accuracy collocation 48.3 collocation syn syn syn+sem syn+sem 60.2 Linguistically richer features improve system accuracy 7
9 Senseval-2 Verbs Results System Accuracy p-value Avg. System SMU JHU KUNLP MaxEnt 60.2 (Human)
10 Senseval-2 verb groupings methodology Groupings of senses done after sense-tagging for Senseval-2 Double blind grouping of each verb by two people Discussion of criteria used for groupings - syntactic and semantic Adjudication of groupings by third person using agreed-upon criteria 9
11 Groupings improve performance Well-defined groupings improve human inter-annotator agreement (71% to 82%) Random grouping produced insignificant improvement in interannotator agreement (71% to 73%) Similar improvement in system score (60% to 70%) 10
12 Chinese WSD (CTB) Lexicon: CETA (Chinese-English Translation Assistance) Dictionary Corpus: Penn Chinese Treebank (100K words) Manual segmentation, pos-tagging, parsing 28 words (multiple verb senses, possibly other pos), most polysemous in 5K-word sample of corpus 3.5 senses/word in corpus Baseline (most frequent sense): 77% 11
13 Contextual predicates (Chinese) Local features: Collocational features: same as for English, plus follows verb feature syntactic features: hassubj, subj, hasobj, obj-p, obj, hasinobj, Comp-VP, VP- Comp, Comp-IP, hasprd semantic features (for verbs only): HowNet noun category for each subject and object Topical features: Same as for English 12
14 System Accuracy and Feature Types (CTB) Feature type Accuracy Std. Dev. collocation collocation (+ pos) collocation + syntax collocation + syntax + semantics baseline
15 Chinese WSD (PDN) Five words with low accuracy and counts in CTB subsequently sense-tagged in People s Daily News (1M words). PDN corpus has manual segmentation, pos-tagging; no parse About 200 sentences/word in PDN 8.2 senses/verb in corpus Baseline (most frequent sense): 58% Automatic segmentation, pos-tagging, parsing 14
16 System Accuracy and Feature Types (PDN, automatic) Feature type Accuracy Std. Dev. collocation collocation (+ pos) collocation + syntax collocation + syntax + semantics baseline
17 System Accuracy and Feature Types (PDN, manual) Feature Type Accuracy Std. Dev. collocation collocation (+ pos) collocation + topic
18 Differences between English and Chinese Higher number of verbs in Chinese than English Lower polysemy per verb for Chinese Many multi-character Chinese verbs Much ambiguitiy in Chinese is at level of word segmentation Lexical collocational information may be sufficient for Chinese 17
19 PropBank II sense-tagging Feasibility study - tag a reasonable set of polysemous words in Eng/Chin CTB determine realistic, concrete sense-tagging goals for next two years Which sense distinctions will be most relevant to IE and MT? how fine-grained do we really need to be? What is the most efficient/accurate way to produce the data? hierarchical tagging? active learning? does hand correcting automatic tagging bias the results? 18
Simple Features for Chinese Word Sense Disambiguation
Simple Features for Chinese Word Sense Disambiguation Hoa Trang Dang, Ching-yi Chia, Martha Palmer, and Fu-Dong Chiou Department of Computer and Information Science University of Pennsylvania htd,chingyc,mpalmer,chioufd
More informationCombining Contextual Features for Word Sense Disambiguation
Proceedings of the SIGLEX/SENSEVAL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, Philadelphia, July 2002, pp. 88-94. Association for Computational Linguistics. Combining
More informationTowards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features
Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features Jinying Chen and Martha Palmer Department of Computer and Information Science, University of Pennsylvania,
More informationCINTIL-PropBank. CINTIL-PropBank Sub-corpus id Sentences Tokens Domain Sentences for regression atsts 779 5,654 Test
CINTIL-PropBank I. Basic Information 1.1. Corpus information The CINTIL-PropBank (Branco et al., 2012) is a set of sentences annotated with their constituency structure and semantic role tags, composed
More informationBuilding a Question Classifier for a TREC-Style Question Answering System
Building a Question Classifier for a TREC-Style Question Answering System Richard May & Ari Steinberg Topic: Question Classification We define Question Classification (QC) here to be the task that, given
More informationCustomizing an English-Korean Machine Translation System for Patent Translation *
Customizing an English-Korean Machine Translation System for Patent Translation * Sung-Kwon Choi, Young-Gil Kim Natural Language Processing Team, Electronics and Telecommunications Research Institute,
More informationPhase 2 of the D4 Project. Helmut Schmid and Sabine Schulte im Walde
Statistical Verb-Clustering Model soft clustering: Verbs may belong to several clusters trained on verb-argument tuples clusters together verbs with similar subcategorization and selectional restriction
More informationA chart generator for the Dutch Alpino grammar
June 10, 2009 Introduction Parsing: determining the grammatical structure of a sentence. Semantics: a parser can build a representation of meaning (semantics) as a side-effect of parsing a sentence. Generation:
More informationChapter 8. Final Results on Dutch Senseval-2 Test Data
Chapter 8 Final Results on Dutch Senseval-2 Test Data The general idea of testing is to assess how well a given model works and that can only be done properly on data that has not been seen before. Supervised
More informationComparing Ontology-based and Corpusbased Domain Annotations in WordNet.
Comparing Ontology-based and Corpusbased Domain Annotations in WordNet. A paper by: Bernardo Magnini Carlo Strapparava Giovanni Pezzulo Alfio Glozzo Presented by: rabee ali alshemali Motive. Domain information
More informationTransition-Based Dependency Parsing with Long Distance Collocations
Transition-Based Dependency Parsing with Long Distance Collocations Chenxi Zhu, Xipeng Qiu (B), and Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science,
More informationTibetan-Chinese Bilingual Sentences Alignment Method based on Multiple Features
, pp.273-280 http://dx.doi.org/10.14257/ijdta.2015.8.4.27 Tibetan-Chinese Bilingual Sentences Alignment Method based on Multiple Features Lirong Qiu School of Information Engineering, MinzuUniversity of
More informationExtraction of Hypernymy Information from Text
Extraction of Hypernymy Information from Text Erik Tjong Kim Sang, Katja Hofmann and Maarten de Rijke Abstract We present the results of three different studies in extracting hypernymy information from
More informationIdentifying Prepositional Phrases in Chinese Patent Texts with. Rule-based and CRF Methods
Identifying Prepositional Phrases in Chinese Patent Texts with Rule-based and CRF Methods Hongzheng Li and Yaohong Jin Institute of Chinese Information Processing, Beijing Normal University 19, Xinjiekou
More informationDetecting Parser Errors Using Web-based Semantic Filters
Detecting Parser Errors Using Web-based Semantic Filters Alexander Yates Stefan Schoenmackers University of Washington Computer Science and Engineering Box 352350 Seattle, WA 98195-2350 Oren Etzioni {ayates,
More informationCustomer Intentions Analysis of Twitter Based on Semantic Patterns
Customer Intentions Analysis of Twitter Based on Semantic Patterns Mohamed Hamroun mohamed.hamrounn@gmail.com Mohamed Salah Gouider ms.gouider@yahoo.fr Lamjed Ben Said lamjed.bensaid@isg.rnu.tn ABSTRACT
More informationEffective Self-Training for Parsing
Effective Self-Training for Parsing David McClosky dmcc@cs.brown.edu Brown Laboratory for Linguistic Information Processing (BLLIP) Joint work with Eugene Charniak and Mark Johnson David McClosky - dmcc@cs.brown.edu
More informationINF5820 Natural Language Processing - NLP. H2009 Jan Tore Lønning jtl@ifi.uio.no
INF5820 Natural Language Processing - NLP H2009 Jan Tore Lønning jtl@ifi.uio.no Semantic Role Labeling INF5830 Lecture 13 Nov 4, 2009 Today Some words about semantics Thematic/semantic roles PropBank &
More informationTowards a RB-SMT Hybrid System for Translating Patent Claims Results and Perspectives
Towards a RB-SMT Hybrid System for Translating Patent Claims Results and Perspectives Ramona Enache and Adam Slaski Department of Computer Science and Engineering Chalmers University of Technology and
More informationNatural Language Database Interface for the Community Based Monitoring System *
Natural Language Database Interface for the Community Based Monitoring System * Krissanne Kaye Garcia, Ma. Angelica Lumain, Jose Antonio Wong, Jhovee Gerard Yap, Charibeth Cheng De La Salle University
More informationThe Proposition Bank: An Annotated Corpus of Semantic Roles
The Proposition Bank: An Annotated Corpus of Semantic Roles Martha Palmer University of Pennsylvania Daniel Gildea. University of Rochester Paul Kingsbury University of Pennsylvania The Proposition Bank
More informationSelf-Training for Parsing Learner Text
elf-training for Parsing Learner Text Aoife Cahill, Binod Gyawali and James V. Bruno Educational Testing ervice, 660 Rosedale Road, Princeton, NJ 0854, UA {acahill, bgyawali, jbruno}@ets.org Abstract We
More informationSpecial Topics in Computer Science
Special Topics in Computer Science NLP in a Nutshell CS492B Spring Semester 2009 Jong C. Park Computer Science Department Korea Advanced Institute of Science and Technology INTRODUCTION Jong C. Park, CS
More informationIdentifying Focus, Techniques and Domain of Scientific Papers
Identifying Focus, Techniques and Domain of Scientific Papers Sonal Gupta Department of Computer Science Stanford University Stanford, CA 94305 sonal@cs.stanford.edu Christopher D. Manning Department of
More informationInteractive Second Language Learning from News Websites
Interactive Second Language Learning from News Websites Tao Chen 1 Naijia Zheng 1 Yue Zhao 1 Muthu Kumar Chandrasekaran 1 Min-Yen Kan 1,2 1 School of Computing, National University of Singapore 2 NUS Interactive
More informationSemantic analysis of text and speech
Semantic analysis of text and speech SGN-9206 Signal processing graduate seminar II, Fall 2007 Anssi Klapuri Institute of Signal Processing, Tampere University of Technology, Finland Outline What is semantic
More informationWord Completion and Prediction in Hebrew
Experiments with Language Models for בס"ד Word Completion and Prediction in Hebrew 1 Yaakov HaCohen-Kerner, Asaf Applebaum, Jacob Bitterman Department of Computer Science Jerusalem College of Technology
More informationAutomatic assignment of Wikipedia encyclopedic entries to WordNet synsets
Automatic assignment of Wikipedia encyclopedic entries to WordNet synsets Maria Ruiz-Casado, Enrique Alfonseca and Pablo Castells Computer Science Dep., Universidad Autonoma de Madrid, 28049 Madrid, Spain
More informationStatistical Machine Translation
Statistical Machine Translation Some of the content of this lecture is taken from previous lectures and presentations given by Philipp Koehn and Andy Way. Dr. Jennifer Foster National Centre for Language
More informationHow the Computer Translates. Svetlana Sokolova President and CEO of PROMT, PhD.
Svetlana Sokolova President and CEO of PROMT, PhD. How the Computer Translates Machine translation is a special field of computer application where almost everyone believes that he/she is a specialist.
More informationQuestion Prediction Language Model
Proceedings of the Australasian Language Technology Workshop 2007, pages 92-99 Question Prediction Language Model Luiz Augusto Pizzato and Diego Mollá Centre for Language Technology Macquarie University
More informationEnglish Grammar Checker
International l Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Issue-3 E-ISSN: 2347-2693 English Grammar Checker Pratik Ghosalkar 1*, Sarvesh Malagi 2, Vatsal Nagda 3,
More informationExploiting Strong Syntactic Heuristics and Co-Training to Learn Semantic Lexicons
Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, July 2002, pp. 125-132. Association for Computational Linguistics. Exploiting Strong Syntactic Heuristics
More informationNatural Language Processing. Part 4: lexical semantics
Natural Language Processing Part 4: lexical semantics 2 Lexical semantics A lexicon generally has a highly structured form It stores the meanings and uses of each word It encodes the relations between
More informationSentiment analysis on news articles using Natural Language Processing and Machine Learning Approach.
Sentiment analysis on news articles using Natural Language Processing and Machine Learning Approach. Pranali Chilekar 1, Swati Ubale 2, Pragati Sonkambale 3, Reema Panarkar 4, Gopal Upadhye 5 1 2 3 4 5
More informationClustering Connectionist and Statistical Language Processing
Clustering Connectionist and Statistical Language Processing Frank Keller keller@coli.uni-sb.de Computerlinguistik Universität des Saarlandes Clustering p.1/21 Overview clustering vs. classification supervised
More informationOpen Domain Information Extraction. Günter Neumann, DFKI, 2012
Open Domain Information Extraction Günter Neumann, DFKI, 2012 Improving TextRunner Wu and Weld (2010) Open Information Extraction using Wikipedia, ACL 2010 Fader et al. (2011) Identifying Relations for
More informationCross-lingual Synonymy Overlap
Cross-lingual Synonymy Overlap Anca Dinu 1, Liviu P. Dinu 2, Ana Sabina Uban 2 1 Faculty of Foreign Languages and Literatures, University of Bucharest 2 Faculty of Mathematics and Computer Science, University
More informationAutomatic Pronominal Anaphora Resolution in English Texts
Computational Linguistics and Chinese Language Processing Vol. 9, No.1, February 2004, pp. 21-40 21 The Association for Computational Linguistics and Chinese Language Processing Automatic Pronominal Anaphora
More informationPiQASso: Pisa Question Answering System
PiQASso: Pisa Question Answering System Giuseppe Attardi, Antonio Cisternino, Francesco Formica, Maria Simi, Alessandro Tommasi Dipartimento di Informatica, Università di Pisa, Italy {attardi, cisterni,
More informationChinese Open Relation Extraction for Knowledge Acquisition
Chinese Open Relation Extraction for Knowledge Acquisition Yuen-Hsien Tseng 1, Lung-Hao Lee 1,2, Shu-Yen Lin 1, Bo-Shun Liao 1, Mei-Jun Liu 1, Hsin-Hsi Chen 2, Oren Etzioni 3, Anthony Fader 4 1 Information
More informationA Mixed Trigrams Approach for Context Sensitive Spell Checking
A Mixed Trigrams Approach for Context Sensitive Spell Checking Davide Fossati and Barbara Di Eugenio Department of Computer Science University of Illinois at Chicago Chicago, IL, USA dfossa1@uic.edu, bdieugen@cs.uic.edu
More informationAcquiring Reliable Predicate-argument Structures from Raw Corpora for Case Frame Compilation
Acquiring Reliable Predicate-argument Structures from Raw Corpora for Case Frame Compilation Daisuke Kawahara, Sadao Kurohashi National Institute of Information and Communications Technology 3-5 Hikaridai
More informationComparing methods for automatic acquisition of Topic Signatures
Comparing methods for automatic acquisition of Topic Signatures Montse Cuadros, Lluis Padro TALP Research Center Universitat Politecnica de Catalunya C/Jordi Girona, Omega S107 08034 Barcelona {cuadros,
More informationThe Role of Sentence Structure in Recognizing Textual Entailment
Blake,C. (In Press) The Role of Sentence Structure in Recognizing Textual Entailment. ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague, Czech Republic. The Role of Sentence Structure
More informationSelected Topics in Applied Machine Learning: An integrating view on data analysis and learning algorithms
Selected Topics in Applied Machine Learning: An integrating view on data analysis and learning algorithms ESSLLI 2015 Barcelona, Spain http://ufal.mff.cuni.cz/esslli2015 Barbora Hladká hladka@ufal.mff.cuni.cz
More informationWhy language is hard. And what Linguistics has to say about it. Natalia Silveira Participation code: eagles
Why language is hard And what Linguistics has to say about it Natalia Silveira Participation code: eagles Christopher Natalia Silveira Manning Language processing is so easy for humans that it is like
More informationPresented to The Federal Big Data Working Group Meetup On 07 June 2014 By Chuck Rehberg, CTO Semantic Insights a Division of Trigent Software
Semantic Research using Natural Language Processing at Scale; A continued look behind the scenes of Semantic Insights Research Assistant and Research Librarian Presented to The Federal Big Data Working
More informationL130: Chapter 5d. Dr. Shannon Bischoff. Dr. Shannon Bischoff () L130: Chapter 5d 1 / 25
L130: Chapter 5d Dr. Shannon Bischoff Dr. Shannon Bischoff () L130: Chapter 5d 1 / 25 Outline 1 Syntax 2 Clauses 3 Constituents Dr. Shannon Bischoff () L130: Chapter 5d 2 / 25 Outline Last time... Verbs...
More informationInteractive Dynamic Information Extraction
Interactive Dynamic Information Extraction Kathrin Eichler, Holmer Hemsen, Markus Löckelt, Günter Neumann, and Norbert Reithinger Deutsches Forschungszentrum für Künstliche Intelligenz - DFKI, 66123 Saarbrücken
More informationIntroduction. BM1 Advanced Natural Language Processing. Alexander Koller. 17 October 2014
Introduction! BM1 Advanced Natural Language Processing Alexander Koller! 17 October 2014 Outline What is computational linguistics? Topics of this course Organizational issues Siri Text prediction Facebook
More informationBuilding the Multilingual Web of Data: A Hands-on tutorial (ISWC 2014, Riva del Garda - Italy)
Building the Multilingual Web of Data: A Hands-on tutorial (ISWC 2014, Riva del Garda - Italy) Multilingual Word Sense Disambiguation and Entity Linking on the Web based on BabelNet Roberto Navigli, Tiziano
More informationContext Grammar and POS Tagging
Context Grammar and POS Tagging Shian-jung Dick Chen Don Loritz New Technology and Research New Technology and Research LexisNexis LexisNexis Ohio, 45342 Ohio, 45342 dick.chen@lexisnexis.com don.loritz@lexisnexis.com
More informationUsing Knowledge Extraction and Maintenance Techniques To Enhance Analytical Performance
Using Knowledge Extraction and Maintenance Techniques To Enhance Analytical Performance David Bixler, Dan Moldovan and Abraham Fowler Language Computer Corporation 1701 N. Collins Blvd #2000 Richardson,
More informationAutomatic Pronominal Anaphora Resolution. in English Texts
Automatic Pronominal Anaphora Resolution in English Texts Tyne Liang and Dian-Song Wu Department of Computer and Information Science National Chiao Tung University Hsinchu, Taiwan Email: tliang@cis.nctu.edu.tw;
More informationApplication of Natural Language Interface to a Machine Translation Problem
Application of Natural Language Interface to a Machine Translation Problem Heidi M. Johnson Yukiko Sekine John S. White Martin Marietta Corporation Gil C. Kim Korean Advanced Institute of Science and Technology
More informationClustering of Polysemic Words
Clustering of Polysemic Words Laurent Cicurel 1, Stephan Bloehdorn 2, and Philipp Cimiano 2 1 isoco S.A., ES-28006 Madrid, Spain lcicurel@isoco.com 2 Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe,
More informationComputer Standards & Interfaces
Computer Standards & Interfaces 35 (2013) 470 481 Contents lists available at SciVerse ScienceDirect Computer Standards & Interfaces journal homepage: www.elsevier.com/locate/csi How to make a natural
More informationChinese-Japanese Machine Translation Exploiting Chinese Characters
Chinese-Japanese Machine Translation Exploiting Chinese Characters CHENHUI CHU, TOSHIAKI NAKAZAWA, DAISUKE KAWAHARA, and SADAO KUROHASHI, Kyoto University The Chinese and Japanese languages share Chinese
More informationNatural Language Processing
Natural Language Processing 2 Open NLP (http://opennlp.apache.org/) Java library for processing natural language text Based on Machine Learning tools maximum entropy, perceptron Includes pre-built models
More informationBridging CAQDAS with text mining: Text analyst s toolbox for Big Data: Science in the Media Project
Bridging CAQDAS with text mining: Text analyst s toolbox for Big Data: Science in the Media Project Ahmet Suerdem Istanbul Bilgi University; LSE Methodology Dept. Science in the media project is funded
More informationDanNet Teaching and Research Perspectives at CST
DanNet Teaching and Research Perspectives at CST Patrizia Paggio Centre for Language Technology University of Copenhagen paggio@hum.ku.dk Dias 1 Outline Previous and current research: Concept-based search:
More informationKnowledge-Based WSD on Specific Domains: Performing Better than Generic Supervised WSD
Knowledge-Based WSD on Specific Domains: Performing Better than Generic Supervised WSD Eneko Agirre and Oier Lopez de Lacalle and Aitor Soroa Informatika Fakultatea, University of the Basque Country 20018,
More informationCONSTRAINING THE GRAMMAR OF APS AND ADVPS. TIBOR LACZKÓ & GYÖRGY RÁKOSI http://ieas.unideb.hu/ rakosi, laczko http://hungram.unideb.
CONSTRAINING THE GRAMMAR OF APS AND ADVPS IN HUNGARIAN AND ENGLISH: CHALLENGES AND SOLUTIONS IN AN LFG-BASED COMPUTATIONAL PROJECT TIBOR LACZKÓ & GYÖRGY RÁKOSI http://ieas.unideb.hu/ rakosi, laczko http://hungram.unideb.hu
More informationApplying Co-Training Methods to Statistical Parsing. Anoop Sarkar http://www.cis.upenn.edu/ anoop/ anoop@linc.cis.upenn.edu
Applying Co-Training Methods to Statistical Parsing Anoop Sarkar http://www.cis.upenn.edu/ anoop/ anoop@linc.cis.upenn.edu 1 Statistical Parsing: the company s clinical trials of both its animal and human-based
More informationExtended Lexical-Semantic Classification of English Verbs
Extended Lexical-Semantic Classification of English Verbs Anna Korhonen and Ted Briscoe University of Cambridge, Computer Laboratory 15 JJ Thomson Avenue, Cambridge CB3 OFD, UK alk23@cl.cam.ac.uk, ejb@cl.cam.ac.uk
More informationModule Catalogue for the Bachelor Program in Computational Linguistics at the University of Heidelberg
Module Catalogue for the Bachelor Program in Computational Linguistics at the University of Heidelberg March 1, 2007 The catalogue is organized into sections of (1) obligatory modules ( Basismodule ) that
More informationConstruction of Thai WordNet Lexical Database from Machine Readable Dictionaries
Construction of Thai WordNet Lexical Database from Machine Readable Dictionaries Patanakul Sathapornrungkij Department of Computer Science Faculty of Science, Mahidol University Rama6 Road, Ratchathewi
More informationHow To Identify And Represent Multiword Expressions (Mwe) In A Multiword Expression (Irme)
The STEVIN IRME Project Jan Odijk STEVIN Midterm Workshop Rotterdam, June 27, 2008 IRME Identification and lexical Representation of Multiword Expressions (MWEs) Participants: Uil-OTS, Utrecht Nicole Grégoire,
More informationOverview of the EVALITA 2009 PoS Tagging Task
Overview of the EVALITA 2009 PoS Tagging Task G. Attardi, M. Simi Dipartimento di Informatica, Università di Pisa + team of project SemaWiki Outline Introduction to the PoS Tagging Task Task definition
More informationLearning to Identify Emotions in Text
Learning to Identify Emotions in Text Carlo Strapparava FBK-Irst, Italy strappa@itc.it Rada Mihalcea University of North Texas rada@cs.unt.edu ABSTRACT This paper describes experiments concerned with the
More informationTREC 2003 Question Answering Track at CAS-ICT
TREC 2003 Question Answering Track at CAS-ICT Yi Chang, Hongbo Xu, Shuo Bai Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China changyi@software.ict.ac.cn http://www.ict.ac.cn/
More informationExploiting Comparable Corpora and Bilingual Dictionaries. the Cross Language Text Categorization
Exploiting Comparable Corpora and Bilingual Dictionaries for Cross-Language Text Categorization Alfio Gliozzo and Carlo Strapparava ITC-Irst via Sommarive, I-38050, Trento, ITALY {gliozzo,strappa}@itc.it
More informationCOMPUTATIONAL DATA ANALYSIS FOR SYNTAX
COLING 82, J. Horeck~ (ed.j North-Holland Publishing Compa~y Academia, 1982 COMPUTATIONAL DATA ANALYSIS FOR SYNTAX Ludmila UhliFova - Zva Nebeska - Jan Kralik Czech Language Institute Czechoslovak Academy
More informationComputer Assisted Language Learning (CALL): Room for CompLing? Scott, Stella, Stacia
Computer Assisted Language Learning (CALL): Room for CompLing? Scott, Stella, Stacia Outline I What is CALL? (scott) II Popular language learning sites (stella) Livemocha.com (stacia) III IV Specific sites
More informationLanguage Model of Parsing and Decoding
Syntax-based Language Models for Statistical Machine Translation Eugene Charniak ½, Kevin Knight ¾ and Kenji Yamada ¾ ½ Department of Computer Science, Brown University ¾ Information Sciences Institute,
More informationQuestion Classification using Head Words and their Hypernyms
Question Classification using Head Words and their Hypernyms Zhiheng Huang EECS Department University of California at Berkeley CA 94720-1776, USA zhiheng@cs.berkeley.edu Marcus Thint Intelligent Systems
More informationSymbiosis of Evolutionary Techniques and Statistical Natural Language Processing
1 Symbiosis of Evolutionary Techniques and Statistical Natural Language Processing Lourdes Araujo Dpto. Sistemas Informáticos y Programación, Univ. Complutense, Madrid 28040, SPAIN (email: lurdes@sip.ucm.es)
More informationWhat s in a Lexicon. The Lexicon. Lexicon vs. Dictionary. What kind of Information should a Lexicon contain?
What s in a Lexicon What kind of Information should a Lexicon contain? The Lexicon Miriam Butt November 2002 Semantic: information about lexical meaning and relations (thematic roles, selectional restrictions,
More informationAutomated Extraction of Security Policies from Natural-Language Software Documents
Automated Extraction of Security Policies from Natural-Language Software Documents Xusheng Xiao 1 Amit Paradkar 2 Suresh Thummalapenta 3 Tao Xie 1 1 Dept. of Computer Science, North Carolina State University,
More informationCoupling an annotated corpus and a morphosyntactic lexicon for state-of-the-art POS tagging with less human effort
Coupling an annotated corpus and a morphosyntactic lexicon for state-of-the-art POS tagging with less human effort Pascal Denis and Benoît Sagot Equipe-project ALPAGE INRIA and Université Paris 7 30, rue
More informationAccelerating and Evaluation of Syntactic Parsing in Natural Language Question Answering Systems
Accelerating and Evaluation of Syntactic Parsing in Natural Language Question Answering Systems cation systems. For example, NLP could be used in Question Answering (QA) systems to understand users natural
More informationOnline Large-Margin Training of Dependency Parsers
Online Large-Margin Training of Dependency Parsers Ryan McDonald Koby Crammer Fernando Pereira Department of Computer and Information Science University of Pennsylvania Philadelphia, PA {ryantm,crammer,pereira}@cis.upenn.edu
More informationA Comparative Study on Sentiment Classification and Ranking on Product Reviews
A Comparative Study on Sentiment Classification and Ranking on Product Reviews C.EMELDA Research Scholar, PG and Research Department of Computer Science, Nehru Memorial College, Putthanampatti, Bharathidasan
More informationTesting Data-Driven Learning Algorithms for PoS Tagging of Icelandic
Testing Data-Driven Learning Algorithms for PoS Tagging of Icelandic by Sigrún Helgadóttir Abstract This paper gives the results of an experiment concerned with training three different taggers on tagged
More informationKybots, knowledge yielding robots German Rigau IXA group, UPV/EHU http://ixa.si.ehu.es
KYOTO () Intelligent Content and Semantics Knowledge Yielding Ontologies for Transition-Based Organization http://www.kyoto-project.eu/ Kybots, knowledge yielding robots German Rigau IXA group, UPV/EHU
More informationSchema documentation for types1.2.xsd
Generated with oxygen XML Editor Take care of the environment, print only if necessary! 8 february 2011 Table of Contents : ""...........................................................................................................
More informationSemantic annotation of requirements for automatic UML class diagram generation
www.ijcsi.org 259 Semantic annotation of requirements for automatic UML class diagram generation Soumaya Amdouni 1, Wahiba Ben Abdessalem Karaa 2 and Sondes Bouabid 3 1 University of tunis High Institute
More informationEfficient Techniques for Improved Data Classification and POS Tagging by Monitoring Extraction, Pruning and Updating of Unknown Foreign Words
, pp.290-295 http://dx.doi.org/10.14257/astl.2015.111.55 Efficient Techniques for Improved Data Classification and POS Tagging by Monitoring Extraction, Pruning and Updating of Unknown Foreign Words Irfan
More informationSentiment Analysis of Twitter Data
Sentiment Analysis of Twitter Data Apoorv Agarwal Boyi Xie Ilia Vovsha Owen Rambow Rebecca Passonneau Department of Computer Science Columbia University New York, NY 10027 USA {apoorv@cs, xie@cs, iv2121@,
More informationEVALITA 07 parsing task
EVALITA 07 parsing task Cristina BOSCO Alessandro MAZZEI Vincenzo LOMBARDO (Dipartimento di Informatica Università di Torino) 1 overview 1. task 2. development data 3. evaluation 4. conclusions 2 task
More informationEvaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles
Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles S Padmaja Dept. of CSE, UCE Osmania University Hyderabad Prof. S Sameen Fatima Dept. of CSE, UCE Osmania University
More informationOpen-domain Commonsense Reasoning Using Discourse Relations from a Corpus of Weblog Stories
Open-domain Commonsense Reasoning Using Discourse Relations from a Corpus of Weblog Stories Matt Gerber Department of Computer Science Michigan State University gerberm2@msu.edu Andrew S. Gordon and Kenji
More informationSemantic Class Induction and Coreference Resolution
Semantic Class Induction and Coreference Resolution Vincent Ng Human Language Technology Research Institute University of Texas at Dallas Richardson, TX 75083-0688 vince@hlt.utdallas.edu Abstract This
More informationAutomatic Speech Recognition and Hybrid Machine Translation for High-Quality Closed-Captioning and Subtitling for Video Broadcast
Automatic Speech Recognition and Hybrid Machine Translation for High-Quality Closed-Captioning and Subtitling for Video Broadcast Hassan Sawaf Science Applications International Corporation (SAIC) 7990
More informationWord Sense Disambiguation as an Integer Linear Programming Problem
Word Sense Disambiguation as an Integer Linear Programming Problem Vicky Panagiotopoulou 1, Iraklis Varlamis 2, Ion Androutsopoulos 1, and George Tsatsaronis 3 1 Department of Informatics, Athens University
More informationONLINE RESUME PARSING SYSTEM USING TEXT ANALYTICS
ONLINE RESUME PARSING SYSTEM USING TEXT ANALYTICS Divyanshu Chandola 1, Aditya Garg 2, Ankit Maurya 3, Amit Kushwaha 4 1 Student, Department of Information Technology, ABES Engineering College, Uttar Pradesh,
More informationHybrid Strategies. for better products and shorter time-to-market
Hybrid Strategies for better products and shorter time-to-market Background Manufacturer of language technology software & services Spin-off of the research center of Germany/Heidelberg Founded in 1999,
More informationGenre distinctions and discourse modes: Text types differ in their situation type distributions
Genre distinctions and discourse modes: Text types differ in their situation type distributions Alexis Palmer and Annemarie Friedrich Department of Computational Linguistics Saarland University, Saarbrücken,
More informationLinguistic Knowledge-driven Approach to Chinese Comparative Elements Extraction
Linguistic Knowledge-driven Approach to Chinese Comparative Elements Extraction Minjun Park Dept. of Chinese Language and Literature Peking University Beijing, 100871, China karmalet@163.com Yulin Yuan
More information