Debasis Ganguly successfully defends PhD thesis on Topical Relevance Models

Today, Debasis Ganguly successfully defended his PhD thesis at Dublin City University.

Topical Relevance Models

by Debasis Ganguly

An inherent characteristic of information retrieval (IR) is that the query expressing a user’s information need is often multi-faceted, that is, it encapsulates more than one specific potential sub-information need. This multi-facetedness of queries manifests itself as a topic distribution in the retrieved set of documents, where each document can be considered as a mixture of topics, one or more of which may correspond to the sub-information needs expressed in the query. In some specific domains of IR, such as patent prior art search, where the queries are full patent articles and the objective is to (in)validate the claims contained therein, the queries themselves are multi-topical in addition to the retrieved set of documents. The overall objective of the research described in this thesis involves investigating techniques to recognize and exploit these multi-topical characteristic of the retrieved documents and the queries in IR and relevance feedback in IR.

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