Improving the effectiveness of information retrieval with local context analysis

By Jinxi Xu, W. Bruce Croft. 2000

654 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search

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Citations in the corpus

Document Expansion by Query Prediction (2019, background): “match problem within these constraints include relevance feedback (Rocchio, 1971), query expansion (Voorhees, 1994; Xu and Croft, 2000), and modeling term relationships using statistical translation (Berger and Lafferty, 1999).”
Probabilistic query expansion using query logs (2002, methodology): “[17] Xu, J. and Croft, W.B. 1996.”
Entity query feature expansion using knowledge base links (2014, methodology): “Another well-known expansion model is Latent Concept Analysis from Xu and Croft [35], which selects ‘concepts’, limited to unigrams and phrase word features based on co-occurrences near query terms in top ranked documents.”
A review of ontology based query expansion (2007, methodology): “(1998) use a manually disambiguated test collection of queries and documents derived from the SEMCOR semantic concordance. Their experiment covers three types of index spaces: original terms; word senses derived from manual disambiguation and finally WordNet synsets. The authors observe that if quer”
Relevance-Based Language Models (2001, result): “When compared to other query expansion techniques [15, 24], our method is attractive because it does not require careful tuning of parameters.”
Interpreting TF-IDF term weights as making relevance decisions (2008, background): “In local context 13:6 H. C. Wu et al. analysis (LCA) [Xu and Croft 2000] or lexical cohesion [Vechtomova et al. 2006], it is implicitly assumed that terms far away from other terms in the middle of the context have negligible impact, and thus such terms are ignored in the LCA and lexical cohesion”
Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization (2014, background): “Typical sources of query expansion terms are pseudorelevant documents (Xu and Croft 2000) or external static resources, such as clickthrough data (Cui et al.”
Exploratory Search: Beyond the Query-Response Paradigm (2009, methodology): “, 1999); (2) query expansion based on analysis of the top-ranked documents (Xu and Croft, 2000); (3) result ranking in link analysis algorithms using FIGURE 4.”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, methodology): “The technique of Xu and Croft [234] is quite obsolete if compared to today’s search engine technology.”
Query Expansion by Mining User Logs (2003, methodology): “Xu and Croft [ 38 ] conducted experiments on very short queries, in which the results showed that query expansion can produce even better”
CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises (2014, methodology): “It scores and selects new terms according to their distribution in the feedback documents (i.e., those retrieved with the initial query), or according to the comparison of their distribution in these documents and the entire collection (Xu and Croft 2000).”
Query Expansion Techniques for Information Retrieval: a Survey (2017, positive): “Reference [278] proposed “local context analysis” technique to extract the query expansion terms from the top documents retrieved in response to the initial query.”
Query dependent pseudo-relevance feedback based on wikipedia (2009, methodology): “For all query expansion methods, pseudo relevance feedback (PRF) is attractive because it requires no user input [21][2][31][3].”