Inferring probability of relevance using the method of logistic regression

By F. Gey. 1994

Subfields: Statistics and Probability, Information Retrieval & Search

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Citations in the corpus

Discriminative models for information retrieval (2004, methodology): “One fundamental modeling issue that distinguishes the present work from that of [5, 7] is that in the latter, each training example is built from feature vectors defined on each query term and document pairs.”
Discriminative Information Retrieval for Question Answering Sentence Selection (2017, unknown): “We generalize Yao et al.’s intuition by casting the problem as an instance of classiﬁcation-based retrieval (Robertson and Sp¨arck Jones, 1976), formalized as a discriminative retrieval model (Cooper et al., 1992; Gey, 1994; Nallapati, 2004) allowing for the use of NLP features.”
Text Categorization Based on Regularized Linear Classification Methods (2001, methodology): “There has been a long history of using logistic regression in information retrieval, as can be seen from the following partial list (Cooper et al. 1992, Fuhr and Pfeifer 1991, Gey 1994, Ittner et al. 1995, Lewis and Gale 1994, Sch¨utze et al. 1995).”
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank (2018, background): “over ranked lists of items during training to optimize a non-differentiable ranking metric, and by that measure fall into one of pointwise, pairwise, or listwise classes of algorithms.Pointwisemethods[13,17,18]approximaterankingtoa classification or regression problem and as such attempt to optimize”
Linear feature-based models for information retrieval (2007, methodology): “In 1994, Gey proposed a logistic regression model for information retrieval (Gey, 1994).”
Learning to rank for information retrieval (2009, unknown): “Example algorithms belonging to the pointwise approach include [24, 25, 26, 31, 33, 34, 49, 53, 73, 78, 90, 114].”
Learning to efficiently rank (2010, methodology): “This has given rise to a steady stream of eﬀectiveness-centric models, such as language models for information retrieval [20], the BM25 model [21], numerous term proximity models [16, 9, 23, 3], and learning to rank [12, 18, 6, 15].”