Using Probabilistic Models of Document Retrieval without Relevance Information

By W. Bruce Croft, David J. Harper. 1979

Subfields: Information Retrieval & Search, Probabilistic & Bayesian Methods

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Citations in the corpus

Introduction to Information Retrieval (2008, unknown): “The Scatter-Gather interface for browsing collections was presented by Cutting et al. (1992). A theoretical framework for analyzing the properties of Scatter/Gather and other information seeking user interfaces is presented by Pirolli (2007).”
Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “This technique was first proposed in reference [71], which employs this technique in a probabilistic model.”
CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises (2014, background): “The query generation step can be followed by query expansion that after searching with an initial query adds to it new terms (Croft and Harper 1979).”
Large scale mining and retrieval of visual data in a multimodal context (2008, methodology): “Gi is a probabilistic inverse document frequency as proposed in [Croft and Harper, 1997], which, unlike idf , assigns negative weights to terms that appear in more than half the documents.”
World-scale mining of objects and events from community photo collections (2008, background): “G i is a probabilistic inverse document frequency as proposed in [6], which, unlike IDF , assigns negative weights to terms that appear in more than half the documents.”
A Language Modeling Approach to Information Retrieval (1998, methodology): “Two well known probabilistic approaches to retrieval are the Robertson and Sparck Jones model [14] and the Croft and Harper model [3].”
Modeling and solving term mismatch for full-text retrieval (2012, methodology): “In addition, Croft and Harper (1979) also proposed to estimate the probability from top retrieved documents from an initial run, and showed performance gain over the baseline methods.”
Improving the effectiveness of information retrieval with local context analysis (2000, unknown): “#169; 2000 ACM 1046-8188/00/0100 0079 $5.00 1.”
Condensed List Relevance Models (2015, background): “Pseudo-relevance feedback refers to the use of an initial retrieval to ﬁnd eﬀective query expansion terms or phrases [1, 5].”
Interpreting TF-IDF term weights as making relevance decisions (2008, methodology): “…idea of using probability theory in information retrieval (IR) has generated a number of different competing or complementary probabilistic models [Damerau 1965; Fuhr 1989; Harter 1974; Cooper and Maron 1978; Margulis 1992], such as the binary independence model (BIM) by Robertson and Sp¨ arck…”