Cross Language Information Retrieval 3. Translation Based on Parallel and Comparable Corpora Parallel Corpora Paragraph Sentence Alignment Utilization of Translation Models in CLIR Embedding Translation Models into CLIR Models Alternative Approaches using Parallel Corpora Exploiting a Parallel Corpus by Pseudo Relevance Feedback Using Latent Semantic Indexing (LSI) Using Comparable Corpora Discussions on CLIR Methods and Resources Mining for Translation Resources and Relations Mining for Parallel Texts Transliteration Mining Translations using Hyperlinks Mining Translations from Monolingual Web Pages 1. Introduction General IR Problems General IR Approaches IR Models Boolean Models Vector Space Model Probabilistic Models Statistical Language Models Query Expansion System Evaluation Language Problems in IR European Languages Word Stemming Decompounding East Asian Languages Chinese and Word Segmentation Japanese and Korean Other Languages The Problems of Cross Language Information Retrieval Query Translation vs. Document Translation Using Pivot Language and Interlingua Approaches to Translation in CLIR The Need for Cross Language and Multilingual IR The History of CLIR 5. A Look into the Future: Toward a Unified View of Monolingual IR and CLIR What has been Achieved Inspiring from Monolingual IR Parallel Between Query Expansion and Query Translation Inspiring Query Translation from Query Expansion An Example References Author Biography Search for information is no longer exclusively limited within the native language of the user but is more and more extended to other languages. This gives rise to the problem of cross language information retrieval (CLIR) whose goal is to find relevant information written in a different language to a query. In addition to the problems of monolingual information retrieval (IR) translation is the key problem in CLIR: one should translate either the query or the documents from a language to another. However this translation problem is not identical to full text machine translation (MT): the goal is not to produce a human readable translation but a translation suitable for finding relevant documents. Specific translation methods are thus required. The goal of this book is to provide a comprehensive description of the specific problems arising in CLIR the solutions proposed in this area as well as the remaining problems. The book starts with a general description of the monolingual IR and CLIR problems. Different classes of approaches to translation are then presented: approaches using an MT system dictionary based translation and approaches based on parallel and comparable corpora. In addition the typical retrieval effectiveness using different approaches is compared. It will be shown that translation approaches specifically designed for CLIR can rival and outperform high quality MT systems. Finally the book offers a look into the future that draws a strong parallel between query expansion in monolingual IR and query translation in CLIR suggesting that many approaches developed in monolingual IR can be adapted to CLIR. The book can be used as an introduction to CLIR. Advanced readers can also find more technical details and discussions about the remaining research challenges in the future. It is suitable to new researchers who intend to carry out research on CLIR 2. Using Manually Constructed Translation Systems and Resources for CLIR Machine Translation Rule Based MT Statistical MT Basic utilization of MT in CLIR Rule Based MT Statistical MT Unknown Word Open the Box of MT Dictionary Based Translation for CLIR Basic Approaches The Term Weighting Problem Coverage of the Dictionary Translation Ambiguity Selection of Translation Words Other Related Approaches 4. Other Methods to Improve CLIR Pre and Post Translation Expansion Fuzzy Matching Combining Translations Transitive Translation Integrating Monolingual and Translingual Relations Discussions