Automatic text processing
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
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
Two-level document ranking using mutual information in natural language information retrieval
Information Processing and Management: an International Journal
A comparison of collocation-based similarity measures in query expansion
Information Processing and Management: an International Journal
Declustering Web Content Indices for Parallel Information Retrieval
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
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An information retrieval (IR) system with query expansion on a low-cost high-performance PC cluster environment is implemented. The IR system stores document sets, it is indexed by the inverted-index-file (IIF), and the vector space model is used as ranking strategy. The query expansion is adding terms into the original query for raising retrieval effectiveness. In this work, the query expansion with the collocation-based similarity measure is used. In our parallel IR system, the inverted-index file (IIF) is partitioned into pieces using the lexical and the greedy declustering methods. For each incoming user's query withm ultiple terms after query expansion, terms are sent to the corresponding nodes that contain the relevant pieces of the IIF to be evaluated in parallel. We study how query performance is affected by query expansion and two declustering methods using two standard Korean test collections. According to the experiments, the greedy method shows about 20% enhancement overall when compared with the lexical method.