Probabilistic query expansion using query logs

By Hang Cui, Ji-rong Wen, Jian-Yun Nie, Wei-Ying Ma. The Web Conference, 2002

524 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d1aff46cb3ae946b3d2c28685710fd492ef96657

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Citations in the corpus

Data services leveraging Bing's data assets (2016, background): “The problem of finding semantically similar/related queries is related to synonym-finding, and is extensively studied in the literature [3, 4, 12].”
Generalizing Translation Models in the Probabilistic Relevance Framework (2016, methodology): “That is, we can either use general knowledge about the terms, extracted from exter-nal resources such as logs [5,9], manual or automatic knowledge-bases [16, 35, 37], or we can use some form of pseudo-relevance feedback (PRF) [1,18,31].”
Context-sensitive information retrieval using implicit feedback (2005, background): “Other related work on using context includes personalized search [1, 3, 4, 7, 10], query log analysis [5], context factors [12], and implicit queries [6].”
Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “Reference [74] uses the query logs to extract probabilistic correlations between query terms and document terms.”
Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007, methodology): “We differ from prior work ( Chen, Park, & Yu, 1998; Eirinaki & Vazirgiannis, 2003; Kamdar & Joshi, 2005) in both the type of information we mine and the uses we find for the patterns mined.”
Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization (2014, methodology): “Recently, Gao et al. (Gao and Nie 2012; Gao, He, and Nie 2010) successfully performed QE by training a statistical translation model on clickthrough data and showed that it performed better than a standard correlation model (Cui et al. 2002).”
Biperpedia: An Ontology for Search Applications (2014, methodology): “In both cases we use internal implementations of techniques proposed for query expansion [7] and spell correction.”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, background): “[69] that queries and documents are rather poorly correlated.”
Personalized query expansion for the web (2007, methodology): “[8] generated word correlations utilizing the probability for query terms to appear in each document, as computed over the search engine logs.”