Enhancing Relevance Models with Adaptive Passage Retrieval

Li, Xiaoyan; Zhu, Zhigang

doi:10.1007/978-3-540-78646-7_44

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…This issue, i.e. the top-ranked initially retrieved documents are not always relevant, is also noted in other work like (Li and Zhu, 2008). In our context, queries are actually questions predefined without caring about the type of data resources (log files) in which one looks for answers.…”

Section: Discussing the Background Methodsmentioning

confidence: 86%

Enhancing passage retrieval in log files by query expansion based on explicit and pseudo relevance feedback

Saneifar

Bonniol²,

Poncelet

et al. 2014

Computers in Industry

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Passage retrieval is usually defined as the task of searching for passages which may contain the answer for a given query. While these approaches are very e cient when dealing with texts, applied to log files (i.e. semi-structured data containing both numerical and symbolic information) they usually provide irrelevant or useless results. Nevertheless one appealing way for improving the results could be to consider query expansions that aim at adding automatically or semi-automatically additional information in the query to improve the reliability and accuracy of the returned results. In this paper, we present a new approach for enhancing the relevancy of queries during a passage retrieval in log files. It is based on two relevance feedback steps. In the first one, we determine the explicit relevance feedback by identifying the context of the requested information within a learning process. The second step is a new kind of pseudo relevance feedback. Based on a novel term weighting measure it aims at assigning a weight to terms according to their relatedness to queries. This measure, called TRQ (Term Relatedness to Query), is used to identify the most relevant expansion terms. The main advantage of our approach is that is can be applied both on log files and documents from general domains. Experiments conducted on real data from logs and documents show that our query expansion protocol enables retrieval of relevant passages.

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Section: Discussing the Background Methodsmentioning

confidence: 86%

Enhancing passage retrieval in log files by query expansion based on explicit and pseudo relevance feedback

Saneifar

Bonniol²,

Poncelet

et al. 2014

Computers in Industry

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show abstract

“…Liu and Croft (2002) also proposed methods for constructing and utilizing passage-based relevance models (Lavrenko and Croft 2001) using the standard passage model. 6 Similar (and somewhat improved) passage-based relevance models were later used by Corrada-Emmanuel et al (2003) and Li and Zhu (2008) for passage and document retrieval. We show in Sect.…”

Section: Related Workmentioning

confidence: 96%

Utilizing passage-based language models for ad hoc document retrieval

Bendersky

Kurland

2009

Inf Retrieval

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To cope with the fact that, in the ad hoc retrieval setting, documents relevant to a query could contain very few (short) parts (passages) with query-related information, researchers proposed passage-based document ranking approaches. We show that several of these retrieval methods can be understood, and new ones can be derived, using the same probabilistic model. We use language-model estimates to instantiate specific retrieval algorithms, and in doing so present a novel passage language model that integrates information from the containing document to an extent controlled by the estimated document homogeneity. Several document-homogeneity measures that we present yield passage language models that are more effective than the standard passage model for basic document retrieval and for constructing and utilizing passage-based relevance models; these relevance models also outperform a document-based relevance model. Finally, we demonstrate the merits in using the document-homogeneity measures for integrating document-query and passage-query similarity information for document retrieval.Keywords Ad hoc document retrieval Á Passage-based language models Á Document homogeneity Á Relevance models Á Passage-based relevance models Portions of this work appeared in Bendersky and Kurland (2008b) and Bendersky (2007).

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“…For window passages, both non-overlapping passages [42], [43] and overlapping passages [2], [3], [44], [45] have been studied in IR. Half-overlapping passages deal with the concern that relevant information may be split across two passages.…”

Section: B Passage-based Retrievalmentioning

confidence: 99%

A Principled Approach Using Fuzzy Set Theory for Passage-Based Document Retrieval

Dang

Luk

Allan

2021

IEEE Trans. Fuzzy Syst.

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We present a novel principled approach to passagebased (document) retrieval using fuzzy set theory. The approach formulates passage score combination according to general relevance decision principles. By operationalizing these principles using aggregation operators of fuzzy set theory, our approach justifies the common heuristics of taking the maximum constituent passage score as the overall document score. Experiments show that this heuristics is only the near best, with some fuzzy set aggregation operators stipulated in our approach being better methods. The significance of our principled approach is the applicability of many passage score combination methods, potentially bringing further performance enhancement. Experiments on several Text REtrieval Conference (TREC) collections demonstrate that our approach performs significantly better than documentbased retrieval. While recent works in the literature mostly employ document-based rather than passage-based retrieval due to the common conception that document length normalization solves the problem of varying document lengths, our results show that document length normalization alone is not sufficient, especially in pseudo-relevance feedback retrieval.

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Enhancing Relevance Models with Adaptive Passage Retrieval

Cited by 8 publications

References 24 publications

Enhancing passage retrieval in log files by query expansion based on explicit and pseudo relevance feedback

Enhancing passage retrieval in log files by query expansion based on explicit and pseudo relevance feedback

Utilizing passage-based language models for ad hoc document retrieval

A Principled Approach Using Fuzzy Set Theory for Passage-Based Document Retrieval

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