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 variables. 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.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-3980 |
| Date | 01 January 2004 |
| Creators | Lavrenko, Victor |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Language | English |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations Available from Proquest |
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