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Title:
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RELEVANCE FEEDBACK USING SEMANTIC ASSOCIATION BETWEEN INDEXING TERMS IN LARGE FREE TEXT CORPUSES |
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Author(s):
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Shahzad Khan , Kenan Azam |
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ISBN:
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972-99353-0-0 |
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Editors:
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Pedro Isaías and Nitya Karmakar |
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Year:
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2004 |
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Edition:
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2 |
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Keywords:
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Computational Linguistics, Information Processing, Relevance Feedback, semantic association, interactive query expansion, machine learning. |
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Type:
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Short Paper |
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First Page:
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1013 |
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Last Page:
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1016 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Relevance feedback has been considered as a means of incorporating learning into information retrieval systems for quite sometime now. This paper discusses the research results of two methodologies for automatic query expansion to incorporate relevance feedback into our probabilistic information retrieval system. The first methodology employs analysis of topmost ranked documents retrieved by the users initial query to form new keyword enriched expanded queries. These queries help the user in focusing his search to a smaller subset of the document collection. The second novel technique developed during the course of this research, resorts to a process similar to text mining employing index term association as an underlying mechanism to expand the query with a semantic structure. We also draw a comparison of the two different methodologies using a human judge, measuring the extent of subject deviation of the expanded query from the initial/original query. The results show that significant improvement can be expected using semantic association between indexing terms in query expansion. |
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