Cross Lingual Information Retrieval Using Data Mining Methods

One of the challenges in cross lingual information retrieval is the retrieval of relevant information for a query expressed in a native language. While retrieval of relevant documents is slightly easier, analyzing the relevance of the retrieved documents and the presentation of the results to the users are non-trivial tasks. A method for information retrieval for a query expressed in a native language is presented in this paper. It uses insights from data mining and intelligent search for formulating the query and parsing the results. It also uses heuristic methods for the categorization of documents in terms of relevance. Our approach compliments the search engine’s inbuilt methods for identifying and displaying the results of queries. A prototype has been developed for analyzing Tamil-English corpora. The initial results have shown that this approach is suitable for on the fly retrieval of documents

Category tree integration by exploiting hierarchical structure.

Lin, Jianfeng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 79-83).Abstracts in English and Chinese.Abstract --- p.i内容摘要 --- p.iiAcknowledgement --- p.iiiTable of Contents --- p.ivList of Figures --- p.viList of Tables --- p.viiChapter Chapter 1. --- Introduction --- p.1Chapter Chapter 2. --- Related Work --- p.6Chapter 2.1. --- Ontology Integration --- p.7Chapter 2.2. --- Schema Matching --- p.10Chapter 2.3. --- Taxonomy Integration as Text Categorization --- p.13Chapter 2.4. --- Cross-lingual Text Categorization & Cross-lingual Information Retrieval --- p.15Chapter Chapter 3. --- Problem Definition --- p.17Chapter 3.1. --- Mono-lingual Category Tree Integration --- p.17Chapter 3.2. --- Integration Operators --- p.19Chapter 3.3. --- Cross-lingual Category Tree Integration --- p.21Chapter Chapter 4. --- Mono-lingual Category Tree Integration Techniques --- p.23Chapter 4.1. --- Category Relationships --- p.23Chapter 4.2. --- Decision Rules --- p.27Chapter 4.3. --- Mapping Algorithm --- p.38Chapter Chapter 5. --- Experiment of Mono-lingual Category Tree Integration --- p.42Chapter 5.1. --- Dataset --- p.42Chapter 5.2. --- Automated Text Classifier --- p.43Chapter 5.3. --- Evaluation Metrics --- p.46Chapter 5.3.1. --- Integration Accuracy --- p.47Chapter 5.3.2. --- Precision and Recall and F1 value of the Three Operators --- p.48Chapter 5.3.3. --- "Precision and Recalls of ""Split""" --- p.48Chapter 5.4. --- Parameter Turning --- p.49Chapter 5.5. --- Experiments Results --- p.55Chapter Chapter 6. --- Cross-lingual Category Tree Integration --- p.60Chapter 6.1. --- Parallel Corpus --- p.61Chapter 6.2. --- Cross-lingual Concept Space Construction --- p.65Chapter 6.2.1. --- Phase Extraction --- p.65Chapter 6.2.2. --- Co-occurrence analysis --- p.65Chapter 6.2.3. --- Associate Constraint Network for Concept Generation --- p.67Chapter 6.3. --- Document Translation --- p.69Chapter 6.4. --- Experiment Setting --- p.72Chapter 6.5. --- Experiment Results --- p.73Chapter Chapter 7. --- Conclusion and Future Work --- p.77Reference --- p.7