A probabilistic inference model for information retrieval
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摘要
Based on the epistemological view of probability, a probabilistic inference model is proposed in this paper. It is argued that some of the problems presented in the conventional probabilistic models may be resolved if one takes the epistemological view instead of the aleatory view of probability. The new model also recognizes the close connections between probabilistic and vector-based approaches. It is explicitly shown that both the standard and generalized vector space models are special cases of the proposed probabilistic inference model. Our method is applicable to any basic concepts chosen from an indexing scheme. Furthermore, it provides a theoretical basis for the vector-based relevance feedback models, and has the additional advantage that no ad hoc parameters are needed. The new approach gives a plausible explanation for adopting a linear measure (discriminant function) for decision making in general, although our discussion is mainly focussed on information retrieval.
论文关键词:Information retrieval,inference,vector space model,probabilistic model,relevance feedback,indexing,query formulation
论文评审过程:Received 2 August 1988, Revised 9 November 1990, Available online 17 June 2003.
论文官网地址:https://doi.org/10.1016/0306-4379(91)90003-R