Flexible pseudo-relevance feedback via selective sampling

Sakai, Tetsuya; Manabe, Toshihiko; Koyama, Makoto

doi:10.1145/1105696.1105699

Cited by 61 publications

(28 citation statements)

References 15 publications

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“…A selective sampling method by Sakai et al [22] skips some top-retrieved documents based on a clustering criterion. The cluster is generated not by document similarity but by the same set of query terms.…”

Section: Related Workmentioning

confidence: 99%

A cluster-based resampling method for pseudo-relevance feedback

Lee

Croft

Allan

2008

Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

130

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Typical pseudo-relevance feedback methods assume the topretrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a clusterbased resampling method to select better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. A higher relevance density will result in greater retrieval accuracy, ultimately approaching true relevance feedback. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.

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Section: Related Workmentioning

confidence: 99%

A cluster-based resampling method for pseudo-relevance feedback

Lee

Croft

Allan

2008

Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

130

View full text Add to dashboard Cite

show abstract

“…The Robustness Index (−1 ≤ RI(q) ≤ 1), also called Reliability of Improvement Index, of a model with respect to a baseline was formulated by Sakai et al in [18] as in Eq 12:…”

Section: Training and Evaluationmentioning

confidence: 99%

Promoting Divergent Terms in the Estimation of Relevance Models

Parapar

Barreiro

2011

Lecture Notes in Computer Science

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Abstract. Traditionally the use of pseudo relevance feedback (PRF) techniques for query expansion has been demonstrated very effective. Particularly the use of Relevance Models (RM) in the context of the Language Modelling framework has been established as a high-performance approach to beat. In this paper we present an alternative estimation for the RM promoting terms that being present in the relevance set are also distant from the language model of the collection. We compared this approach with RM3 and with an adaptation to the Language Modelling framework of the Rocchio's KLD-based term ranking function. The evaluation showed that this alternative estimation of RM reports consistently better results than RM3, showing in average to be the most stable across collections in terms of robustness.

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“…Some research even questions the usefulness of BRF in general [9]. Many approaches have been proposed to increase the overall IR performance of BRF, for example by adapting the number of feedback terms and documents per topic [10], by selecting only good feedback terms [11,12], or by increasing diversity of terms in pseudo-relevant documents by skipping feedback documents [13]. TREC experiments with BRF use conservative settings for the number of feedback terms and documents (see [7,14]) using less than 10 documents and 10-30 feedback terms to obtain the best IR effectiveness.…”

Section: Related Workmentioning

confidence: 99%

Query Expansion for Language Modeling Using Sentence Similarities

Ganguly

Leveling

Jones

2011

Lecture Notes in Computer Science

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Abstract. We propose a novel method of query expansion for Language Modeling (LM) in Information Retrieval (IR) based on the similarity of the query with sentences in the top ranked documents from an initial retrieval run. In justification of our approach, we argue that the terms in the expanded query obtained by the proposed method roughly follow a Dirichlet distribution which, being the conjugate prior of the multinomial distribution used in the LM retrieval model, helps the feedback step. IR experiments on the TREC ad-hoc retrieval test collections using the sentence based query expansion (SBQE) show a significant increase in Mean Average Precision (MAP) compared to baselines obtained using standard term-based query expansion using LM selection score and the Relevance Model (RLM). The proposed approach to query expansion for LM increases the likelihood of generation of the pseudo-relevant documents by adding sentences with maximum term overlap with the query sentences for each top ranked pseudorelevant document thus making the query look more like these documents. A per topic analysis shows that the new method hurts less queries compared to the baseline feedback methods, and improves average precision (AP) over a broad range of queries ranging from easy to difficult in terms of the initial retrieval AP. We also show that the new method is able to add a higher number of good feedback terms (the golden standard of good terms being the set of terms added by True Relevance Feedback). Additional experiments on the challenging search topics of the TREC-2004 Robust track show that the new method is able to improve MAP by 5.7% without the use of external resources and query hardness prediction typically used for these topics.

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Flexible pseudo-relevance feedback via selective sampling

Cited by 61 publications

References 15 publications

A cluster-based resampling method for pseudo-relevance feedback

A cluster-based resampling method for pseudo-relevance feedback

Promoting Divergent Terms in the Estimation of Relevance Models

Query Expansion for Language Modeling Using Sentence Similarities

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