Query expansion using local and global document analysis

Xu, Jinxi; Croft, W. Bruce

doi:10.1145/243199.243202

Cited by 841 publications

(622 citation statements)

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“…Query expansion generates one or more hypotheses for query refinement by recognizing possible interpretations of a query, based on knowledge coming either directly from the corpus [2,29,24,10,3] or from Web data or personal profiles in the case of Web search [30,13,12,21]. Query expansion techniques select suggestions for query refinement either interactively or automatically [11].…”

Section: Related Workmentioning

confidence: 99%

Query Refinement Using Conversational Context: A Method and an Evaluation Resource

Habibi

Popescu-Belis

2015

Natural Language Processing and Information Systems

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Abstract. This paper introduces a query refinement method applied to queries asked by users during a meeting or a conversation. Current approaches suffer from poor quality to achieve this goal, but we argue that their performance could be improved by focusing on the local context of the conversation. The proposed technique first represents the local context by extracting keywords from the transcript of the conversation. It then expands the queries with keywords that best represent the topic of the query (e.g. pairs of expansion keywords together with a weight indicating their topical similarity to the query). Moreover, we present a dataset called AREX and an evaluation metric. We compared our query expansion approach with other methods, on topics extracted from the AREX dataset and based on relevance judgments collected in a crowdsourcing experiment. The comparisons indicate the superiority of our method on both manual and ASR transcripts of the AMI Meeting Corpus.

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Section: Related Workmentioning

confidence: 99%

Query Refinement Using Conversational Context: A Method and an Evaluation Resource

Habibi

Popescu-Belis

2015

Natural Language Processing and Information Systems

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“…Relevance feedback can be applied to better capture a user's information need [1,7,15]. In a language modeling context, this can be performed by re-estimating the query model, i.e., P(t|θ Q ) in Eq.…”

Section: Query Modelingmentioning

confidence: 99%

Concept Models for Domain-Specific Search

Meij

Rijke

2009

Lecture Notes in Computer Science

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Abstract. We describe our participation in the 2008 CLEF Domain-specific track. We evaluate blind relevance feedback models and concept models on the CLEF domain-specific test collection. Applying relevance modeling techniques is found to have a positive effect on the 2008 topic set, in terms of mean average precision and precision@10. Applying concept models for blind relevance feedback, results in even bigger improvements over a query-likelihood baseline, in terms of mean average precision and early precision.

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“…It consists of the expansion of the content of the query by adding the terms that are semantical correlated with the original terms of the query [12]. Several works demonstrated the enhanced performance of IR systems that implement query expansion approaches [19] [3] [5]. However, the query expansion approach has to be used carefully because, as demonstrated in [8], expansion might degrade the performance of some individual queries.…”

Section: Introductionmentioning

confidence: 99%