Computational Linguistics and Intelligent Text Processing

12th International Conference, CICLing 2011, Tokyo, Japan, February 20-26, 2011. Proceedings, Part II

Editors:

ISBN: 978-3-642-19436-8 (Print) 978-3-642-19437-5 (Online)

Table of contents (42 chapters)

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  1. Front Matter

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  2. Machine Translation and Multilingualism

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      Pages 1-12

      Ontology Based Interlingua Translation

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      Pages 13-28

      Phrasal Equivalence Classes for Generalized Corpus-Based Machine Translation

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      Pages 29-40

      A Multi-view Approach for Term Translation Spotting

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      Pages 41-54

      ICE-TEA: In-Context Expansion and Translation of English Abbreviations

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      Pages 55-67

      Word Segmentation for Dialect Translation

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      Pages 68-79

      TEP: Tehran English-Persian Parallel Corpus

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      Pages 80-92

      Effective Use of Dependency Structure for Bilingual Lexicon Creation

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      Pages 93-105

      Online Learning via Dynamic Reranking for Computer Assisted Translation

  3. Information Extraction and Information Retrieval

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      Pages 106-126

      Learning Relation Extraction Grammars with Minimal Human Intervention: Strategy, Results, Insights and Plans

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      Pages 127-138

      Using Graph Based Method to Improve Bootstrapping Relation Extraction

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      Pages 139-150

      A Hybrid Approach for the Extraction of Semantic Relations from MEDLINE Abstracts

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      Pages 151-162

      An Active Learning Process for Extraction and Standardisation of Medical Measurements by a Trainable FSA

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      Pages 163-176

      Topic Chains for Understanding a News Corpus

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      Pages 177-187

      From Italian Text to TimeML Document via Dependency Parsing

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      Pages 188-201

      Self-adjusting Bootstrapping

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      Pages 202-211

      Story Link Detection Based on Event Words

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      Pages 212-220

      Ranking Multilingual Documents Using Minimal Language Dependent Resources

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      Pages 221-233

      Measuring Chinese-English Cross-Lingual Word Similarity with HowNet and Parallel Corpus

  4. Text Categorization and Classification

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      Pages 234-243

      Comparing Manual Text Patterns and Machine Learning for Classification of E-Mails for Automatic Answering by a Government Agency

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      Pages 244-253

      Using Thesaurus to Improve Multiclass Text Classification

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