Quary Expansion Using Local and Global Document Analysis

By Jinxi Xu, W. Bruce Croft. 1996

Subfields: Information Retrieval & Search

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Citations in the corpus

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.”
Relevance-based Word Embedding (2017, background): “As is widely known in the information retrieval literature [11, 38], there is a big dierence between the unigram distribution of words on sub-topics of a collection and the unigram distribution estimated from the whole collection.”
Task-Oriented Query Reformulation with Reinforcement Learning (2017, background): “It typically expands the original query by adding terms from, for instance, dictionaries of synonyms such as WordNet (Miller, 1995), or from the initial set of retrieved documents (Xu and Croft, 1996).”
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (2019, neutral): “PRF has been shown to be effective in various retrieval settings (Xu and Croft, 1996).”
Semantic Speech Retrieval With a Visually Grounded Model of Untranscribed Speech (2017, methodology): “ther the keyword occurs exactly in the document. This can be accomplished using query expansion, where additional words or phrases similar in meaning to the query are used to match relevant documents [68], [69]. Classic approaches use co-occurrences of words, or resources such as WordNet [70], to ex”
Query Expansion Techniques for Information Retrieval: a Survey (2017, methodology): “Corpus or Collection based data sources Terms collection from specific domain knowledge Jones 1971 [133], Attar and Fraenkel 1977 [16], Peat and Willett 1991 [215], Crouch and Yang 1992 [75], Qiu and Frei 1993 [220], Xu and Croft 1996[284], Gauch et al.”
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions (2020, methodology): “Inspired by the idea of pseudo-relevance feedback (Xu and Croft 2017), Das et al. (2019b) also leverages entitylink to find more supporting evidence.”
Query Expansion with Locally-Trained Word Embeddings (2016, methodology): “e nuances of topic-specic language better than global embeddings. There is substantial evidence that global methods underperform local methods for information retrieval tasks such as query expansion [48], latent semantic analysis [20, 37, 39], cluster-based retrieval [41, 42, 47], and term clusterin”