Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Word association norms, mutual information, and lexicography
Computational Linguistics
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Translating collocations for bilingual lexicons: a statistical approach
Computational Linguistics
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Querying across languages: a dictionary-based approach to multilingual information retrieval
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting clustering and phrases for context-based information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Resolving ambiguity for cross-language retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Cross-language information retrieval with the UMLS metathesaurus
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Translingual information retrieval: learning from bilingual corpora
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Re-ranking model based on document clusters
Information Processing and Management: an International Journal
Information Retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
An algorithm for finding noun phrase correspondences in bilingual corpora
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Using mutual information to resolve query translation ambiguities and query term weighting
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A query-based cross-language diagnosis tool for distributed decision making support
Computers and Industrial Engineering
Query translation-based cross-language print defect diagnosis based on the fuzzy Bayesian model
Journal of Intelligent Manufacturing
Evaluating Google queries based on language preferences
Journal of Information Science
A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback
Information Processing and Management: an International Journal
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This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in cross-language information retrieval (CLIR) such as Korean-to-English and Japanese-to-English CLIR. The main objective of this paper shows that document clusters can effectively resolve the ambiguities tremendously increased in translated queries as well as take into account the context of all the terms in a document. In the framework we propose, a query in Korean/Japanese is first translated into English by looking up bilingual dictionaries, then documents are retrieved for the translated query terms based on the vector space retrieval model or the probabilistic retrieval model. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is recalculated by using the clusters. In the experiment based on TREC CLIR test collection, our method achieved 39.41% and 36.79% improvement for translated queries without ambiguity resolution in Korean-to-English CLIR, and 17.89% and 30.46% improvements in Japanese-to-English CLIR, on the vector space retrieval and on the probabilistic retrieval, respectively. Our method achieved 12.30% improvement for all translation queries, compared with blind feedback for the probabilistic retrieval in Korean-to-English CLIR. These results indicate that cluster analysis help to resolve ambiguity.