A Survey of Automatic Query Expansion in Information Retrieval

By Claudio Carpineto, Giovanni Romano. 2012

1,213 citations recorded by the source corpus.

Subfields: Information Systems, Information Retrieval & Search, Signal Processing

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Citations in the corpus

How Can We Know What Language Models Know? (2019, methodology): “This is conceptually similar to query expansion techniques used in information retrieval that reformulate a given query to improve retrieval performance (Carpineto and Romano, 2012).”
Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2 (2021, background): “Recent studies have explored two directions for dynamically adjusting and expanding LM queries, which are conceptually related to automatic query expansion in (classic) information retrieval systems [Carpineto and Romano, 2012]: 1.”
Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization (2014, background): “A well-known, effective strategy to solve this issue is to perform query expansion (QE) (Carpineto and Romano 2012), i.”
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-Shot Learning (2019, background): “ations have been equally under-studied by most information retrieval researchers. Historically, ad-hoc retrieval systems have been primarily designed, trained, and evaluated on English corpora (e.g., [1,5,6,23]). More recently, a new wave of supervised state-of-the-art ranking models have been propo”
Query Expansion Techniques for Information Retrieval: a Survey (2017, methodology): “Hence, reference [58] suggested that instead of considering a particular method, one can come up with a superior expanded query model (calculated using Jelinek-Mercer interpolation [129]) given as:”
Integrating and Evaluating Neural Word Embeddings in Information Retrieval (2015, methodology): “Query expansion [8] is the process of adding terms to a seed query to improve its retrieval effectiveness: conceptually, the automatic expansion process shares the same key intuition as translation language models.”
Pseudo-Relevance Feedback Based on Matrix Factorization (2016, background): “The weight function can potentially be enhanced using these kinds of information that have been shown to be useful in the PRF task [3, 21, 23, 40].”
Estimating Embedding Vectors for Queries (2016, background): “Therefore, although query expansion is not a new technique for improving retrieval effectiveness, it is still a challenging task [7].”
Rethinking Query Expansion for BERT Reranking (2020, background): “Classic query expansion techniques expand the original query with terms selected from related documents; terms are usually added as a bag-of-words [1, 9] .”
TVSum: Summarizing web videos using titles (2015, methodology): “Lastly, we create additional queries by taking n-grams around each token [8]; we set n = 3.”
Embedding-based Query Language Models (2016, background): “Although a number of data-driven query expansion methods, such as [3], can improve the average retrieval performance, they are shown to be unstable across queries [4, 6].”
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013, background): “Many approaches assume that the advertiser has provided a seed set of bid phrases from the ad landing page and focus on expanding the seed set [1] [2] [10] [11] [23] [28] [29].”
Deep Reinforced Query Reformulation for Information Retrieval (2020, background): “…by previous work in selective pseudo-relevance feedback [15]: indeed, the effectiveness of pseudo-relevance feedback is sensitive to the quality of the initial retrieval results [5], and therefore query performance predictors [4, 14] can be used to identify when it suitable to apply PRF [15].”