Accession Number : ADA440135


Title :   A Generative Theory of Relevance


Descriptive Note : Doctoral thesis


Corporate Author : MASSACHUSETTS UNIV AMHERST


Personal Author(s) : Lavrenko, Victor


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a440135.pdf


Report Date : Sep 2004


Pagination or Media Count : 131


Abstract : We present a new theory of relevance for the field of Information Retrieval. Relevance is viewed as a generative process and we hypothesize that both user queries and relevant documents represent random observations from that process. Based on this view we develop a formal retrieval model that has direct applications to a wide range of search scenarios. The new model substantially outperforms strong baselines on the tasks of ad-hoc retrieval, cross-language retrieval, handwriting retrieval, automatic image annotation, video retrieval and topic detection and tracking. Empirical success of our approach is due to a new technique we propose for modeling exchangeable sequences of discrete random variable. The new technique represents an attractive counterpart to existing formulations, such as multinomial mixtures, pLSI and LDA:it is effective, easy to train, and makes no assumptions about the geometric structure of the data.


Descriptors :   *INFORMATION RETRIEVAL , INFORMATION SYSTEMS , THEORY , THESES , SEARCHING , DOCUMENTS


Subject Categories : Information Science


Distribution Statement : APPROVED FOR PUBLIC RELEASE