Query Generation Using Semantic Features
This paper describes a query generation using semantic features to represent the information demand of users for question answering and information retrieval. One of fundamental reasons why unwanted results are included in responses of all information retrieval systems is because queries do not exactly represent the information demand of users. To solve this problem, a query generaton using the semantic feature is intended to extract semantic features which appear commonly in natural language questions of similar type and utilize them for question answering and information retrieval. We extract semantic features from natural language questions using a grammar and generate queries which represent enough information demands of users using semantic features and syntactic structures. For performance improvement of question answering and information retrieval, we introduce a query-document similarity used to rank documents which include generated queries in the high position. We evaluated our mechanism using 100 queries about a person in the web. There was a notable improvement in the precision at N documents when our approach is applied. Especially, we found that an efficient document retrieval is possible by a question analysis based on semantic features on natural language questions which are comparatively short but fully expressing the information demand of users.
KeywordsQuestion Analysis Query Generation Semantic Feature Question Answering Information Retrieval
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- 1.Bilotti, M.W., Katz, B., Lin, J.: What Works Better for Question Answering: Stemming or Morphological Query Expansion? In: IR4QA: Information Retrieval for Question Answering, A SIGIR 2004 Workshop (2004)Google Scholar
- 3.Dobrow, B.V., Loukachevitch, N.V., Yudina, T.N.: Conceptual Indexing Using thematic Representation of Texts. TREC-6 (1997)Google Scholar
- 5.Zhai, C.: Fast Statistical Parsing of Noun Phrases for Document Indexing. In: Proceedings of the Fifth Conference of Applied Natural Language Processing (1997)Google Scholar
- 6.Myaeng, S.H.: Current Status and New Directions of Information Retrieval Technique. Communications of the Korea Information Science Society 24(4), 6–14 (2004)Google Scholar
- 8.Salton, G.: Automatic Text Processing. Addison-Wesley, Reading (1989)Google Scholar
- 9.Maron, M.E., Kuhns, J.L.: On relevance, probabilistic indexing and information retrieval. Journal of the ACM, 216–244 (1960)Google Scholar
- 10.Voorhees, E.: Query Expansion using Lexical Semantic Relation. In: Proceedings of the 17th ACM-SIGIR Conference, pp. 61–69 (1994)Google Scholar
- 11.Fitzpatrick, L., Dent, M.: Automatic Feedback Using Past Queries: Social Searching? In: Proc. 20’th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 306–313 (1997)Google Scholar
- 12.Mandela, R., Tokunage, T., Tanaka, H.: Combining Multiple Evidence from Different Types of Thesaurus for Query Expansion. In: Proceedings of the 22nd Annual International ACM SIGIR Conference, pp. 15–19 (1999)Google Scholar
- 13.Moldovan, D., Mihalcea, R.: Using WordNet and Lexical Operators to Improve Internet Searches. In: Proceedings of IEEE Internet Computing, pp. 34–43 (2000)Google Scholar
- 14.Zukerman, I., Raskutti, B.: Lexical Query Paraphrasing for Document Retrieval. In: The 17th International Conference on Computational Linguistics, COLING 2002 (2002)Google Scholar