Cross-Language Information Retrieval

Description

Cross-Language Information Retrieval Synthesis Lectures in Human Language Technologies ABSTRACT Keywords Dedication Contents Preface Acknowledgement chapter 1 Introduction 1.1 General IR problems 1.2 General IR approaches 1.2.1 IR Models 1.2.1.1 Boolean Models 1.2.1.2 Vector Space Model 1.2.1.3 Probabilistic Models 1.2.1.4 Statistical Language Models 1.2.2 Query Expansion 1.2.3 System Evaluation 1.3 Language problems in IR 1.3.1 European Languages 1.3.1.1 Word Stemming 1.3.1.2 Decompounding 1.3.2 East Asian Languages 1.3.2.1 Chinese and Word Segmentation 1.3.2.2 Japanese and Korean 1.3.3 Other Languages 1.4 The problems of cross-language information retrieval 1.4.1 Query Translation vs. Document Translation 1.4.2 Using Pivot Language and Interlingua 1.5 Approaches to translation in CLIR 1.6 The need for cross-language and multilingual IR 1.7 The history of CLIR chapter 2 Using Manually Constructed Translation Systems and Resources for CLIR 2.1 Machine translation 2.1.1 Rule-Based MT 2.1.2 Statistical MT 2.2 Basic utilization of MT in CLIR 2.2.1 Rule-Based MT 2.2.2 Statistical MT 2.2.3 Unknown Word 2.3. Open the box of MT 2.4. Dictionary-based Translation for CLIR 2.4.1 Basic Approaches 2.4.2 The Term Weighting Problem 2.4.3 Coverage of the Dictionary 2.4.4 Translation Ambiguity 2.4.5 Selection of Translation Words 2.4.6 Other Related Approaches 2.4.6.1 Phrase-Based and Structured Query Translation 2.4.6.2 Using Multilingual Thesauri chapter 3 Translation Based on Parallel and Comparable Corpora 3.1 Parallel corpora 3.2 Paragraph/sentence alignment 3.3 Utilization of translation models in CLIR 3.4 Embedding translation models into CLIR models 3.5 Alternative approaches using parallel corpora 3.5.1 Exploiting a Parallel Corpus by Pseudo-Relevance Feedback 3.5.2 Using Latent Semantic Indexing (LSI) 3.5.3 Using Comparable Corpora 3.6 Discussions on CLIR methods and resources 3.7 Mining for translation resources and relations 3.7.1 Mining for Parallel Texts 3.7.2 Transliteration 3.7.3 Mining Translations Using Hyperlinks 3.7.4 Mining Translations from Monolingual Web Pages chapter 4 Other Methods to Improve CLIR 4.1 Pre- and post-translation expansion 4.2 Fuzzy matching 4.3 Combining translations 4.4 Transitive translation 4.5 Integrating monolingual and translingual relations 4.6 Discussions chapter 5 A Look into the Future: Toward a Unified View of Monolingual IR and CLIR 5.1 What has been achieved? 5.2 Inspiring from monolingual IR 5.2.1 Parallel Between Query Expansion and Query Translation 5.2.2 Inspiring Query Translation from Query Expansion-An Example References Author Biography