Relevance-Based Language Models

By Victor Lavrenko, W. Bruce Croft. 2001

1,857 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval (2020, background): “Addressing this vocabulary mismatch is a core research topic in IR [8, 20, 21, 22].”
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering (2019, methodology): “Query Reformulation by augmenting the original query with terms from the top-k retrieved document (Xu & Croft, 1996; Lavrenko & Croft, 2001) has proven to be very effective in information retrieval.”
From Frequency to Meaning: Vector Space Models of Semantics (2010, background): “Examples of semantic annotation are disambiguating abbreviations in queries (Wei, Peng, & Dumoulin, 2008) and finding query keyword associations (Lavrenko & Croft, 2001; Cao, Nie, & Bai, 2005).”
Relevance-based Word Embedding (2017, methodology): “In the next set of experiments, we consider the methods that use the top retrieved documents for query expansion: the relevance model (RM3) [1, 20] as a state-of-the-art pseudo-relevance feedback model, and the local embedding approach recently proposed by Diaz et al. [11] with the general idea of…”
Task-Oriented Query Reformulation with Reinforcement Learning (2017, methodology): “A popular instantiation of a local method is the relevance model, which incorporates pseudo-relevance feedback into a language model form [10].”
Generation-Augmented Retrieval for Open-Domain Question Answering (2020, background): “, relevance models with (pseudo) relevance feedback (Lavrenko and Croft, 2001; Abdul-Jaleel et al., 2004).”
Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval (2019, methodology): “The predictions are used to generate weighted queries that can be used with widely-used retrieval models such as BM25 [26] and query likelihood [15].”
Anserini (2018, methodology): “Anserini provides a reference implementation of the RM3 variant [1] of relevance models [27] using our reranking API.”
Anserini: Enabling the Use of Lucene for Information Retrieval Research (2017, methodology): “Anserini provides a reference implementation of the RM3 variant of relevance models [9], built as a reranking module in the multi-stage architecture described above.”
Context-Aware Document Term Weighting for Ad-Hoc Search (2020, methodology): “Statistical machine translation models have not been used much because they are difficult to train due to data sparsity, and were not more effective than tf -based retrieval with pseudo-relevance feedback [22] in most situations.”
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (2019, neutral): “The Rocchio’s algorithm on top of the TF-IDF retrieval model (P RF - TFIDF ) (Rocchio, 1971) and the relevance model (RM3) based on the language modeling framework in information retrieval (P RF - RM ) (Lavrenko and Croft, 2001).”
Finding similar questions in large question and answer archives (2005, methodology): “[11] proposed a cross-lingual information retrieval model based on relevance models [12].”
Implicit user modeling for personalized search (2005, neutral): “In a language modeling approach, we may also regard the query unigram language model [12, 29] or the relevance model [14] as a term vector representation of the user’s information need.”
Applications of Topic Models (2017, methodology): “Two major directions have been explored: query language models [Zhai and Lafferty, 2001b] and relevance models [Lavrenko and Croft, 2001].”
Complementing Lexical Retrieval with Semantic Residual Embedding (2020, background): “One successful approach to bridge the vocabulary gap between queries and documents is query expansion (Lavrenko and Croft, 2001).”
Find-similar: similarity browsing as a search tool (2006, positive): “Figure 1 shows the Indri query produced by Metzler and Croft’s dependence models for TREC topic 301, “international organized crime.””
A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval (2014, background): “Modeling contextual information in search queries and documents is a long-standing research topic in IR [11][25][12][26][2][22][24].”
Answer Interaction in Non-factoid Question Answering Systems (2019, neutral): “In practice, pseudo-relevance feedback (PRF) [6, 14, 16]”
Query Expansion Using Word Embeddings (2016, methodology): “In addition, we present an approach to integrating the terms selected using word embeddings with an eﬀective pseudo-feedback-based method: the relevance model [10].”
Estimating Embedding Vectors for Queries (2016, methodology): “PRF has been proven to be highly effective in improving retrieval performance [23, 36, 38].”