PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Topic-Specific Scoring of Documents for Relevant Retrieval
Wray Buntine
In: ICML 2005 Workshop 4: Learning in Web Search, 7 August 2005, Bonn, Germany.


There has been mixed success in applying semantic component analysis (LSA, PLSA, discrete PCA, etc.) to information retrieval. Here we combine topic-specific link analysis with discrete PCA (a semantic component method) to develop a topic relevancy score for information retrieval that is used in post-filtering documents retrieved via regular Tf.Idf methods. When combined with a novel and intuitive ``topic by example'' interface, this allows a user-friendly manner to include topic relevance into search. To evaluate the resultant topic and link based scoring, a demonstration has been built using the Wikipedia, the public domain encyclopedia on the web.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:1247
Deposited By:Wray Buntine
Deposited On:28 November 2005