Automatic text processing
Phase-based information retrieval
Information Processing and Management: an International Journal
Natural language information retrieval: progress report
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
High performance question/answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Fast statistical parsing of noun phrases for document indexing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Experiments with open-domain textual Question Answering
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
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This paper describes a concept-based question analysis for an efficient document ranking. Our idea is that we can rank efficiently documents containing answers for questions when we use well-defined concepts because concepts occurred in questions of same answer type are similar. That is, we can retrieve more relevant documents if we know the syntactic and semantic role of each word or phrase in question. For each answer type, we define a concept rule which contains core concepts occurred in questions of that answer type. Concept-based question analysis is a process which tags concepts to morphological analysis result of a user's question, determines the answer type, and extracts untagged concepts from it using a matched concept rule. Empirical results show that our concept-based question analysis can rank documents more efficiently than any other conventional approach. Also, concept-based approach has additional merits that it is language universal model, and can be combined with arbitrary conventional approaches.