This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in Korean-to-English cross-language information retrieval. In the framework we propose, a query in Korean is first translated into English by looking up Korean-English dictionary, then documents are retrieved based on the vector space retrieval for the translated query terms. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is re-calculated by using clusters. In experiment on TREC-6 CLIR test collection, our method achieved 28.29% performance improvement for translated queries without ambiguity resolution for queries. This corresponds to 97.27% of the monolingual performance for original queries. When we combine our method with query ambiguity resolution, our method even outperforms the monolingual retrieval.
KyungSoon et al. REF in 2002 proposed a method to implicitly resolve ambiguities in Korean-English CLIR system using dynamic incremental clustering approach means the clusters are incrementally created for the top ranked documents for a particular query and next time when the same query will fired than the weight of each retrieved document is recalculated by using these clusters.
Implicit Ambiguity Resolution Using Incremental Clustering In Korean-To-English Cross-Language Information Retrieval
International Conference On Computational Linguistics
Computer Science