Term proximity scoring for ad-hoc retrieval on very large text collections

Büttcher, Stefan; Clarke, Charles L. A.; Lushman, Brad

doi:10.1145/1148170.1148285

Cited by 126 publications

(86 citation statements)

References 2 publications

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“…The proximity IR model [20,21] does not improve the performance significantly while the BM25P model [22] improves the performance but it is sensitive to the window size.…”

Section: A Proximity-base Information Retrieval Modelmentioning

confidence: 92%

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A Novel Information Retrieval Approach using Query Expansion and Spectral-based

Alnofaie¹,

Dahab²,

Kamal³

2016

ijacsa

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Abstract-Most of the information retrieval (IR) models rank the documents by computing a score using only the lexicographical query terms or frequency information of the query terms in the document. These models have a limitation as they does not consider the terms proximity in the document or the term-mismatch or both of the two. The terms proximity information is an important factor that determines the relatedness of the document to the query. The ranking functions of the Spectral-Based Information Retrieval Model (SBIRM) consider the query terms frequency and proximity in the document by comparing the signals of the query terms in the spectral domain instead of the spatial domain using Discrete Wavelet Transform (DWT). The query expansion (QE) approaches are used to overcome the word-mismatch problem by adding terms to query, which have related meaning with the query. The QE approaches are divided to statistical approach Kullback-Leibler divergence (KLD) and semantic approach P-WNET that uses WordNet. These approaches enhance the performance. Based on the foregoing considerations, the objective of this research is to build an efficient QESBIRM that combines QE and proximity SBIRM by implementing the SBIRM using the DWT and KLD or P-WNET. The experiments conducted to test and evaluate the QESBIRM using Text Retrieval Conference (TREC) dataset. The result shows that the SBIRM with the KLD or P-WNET model outperform the SBIRM model in precision (P@), R-precision, Geometric Mean Average Precision (GMAP) and Mean Average Precision (MAP).

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“…The proximity IR model [20,21] does not improve the performance significantly while the BM25P model [22] improves the performance but it is sensitive to the window size.…”

Section: A Proximity-base Information Retrieval Modelmentioning

confidence: 92%

“…Some research combines the proximity information to frequency scoring function [20,21,22]. The proximity IR model [20,21] does not improve the performance significantly while the BM25P model [22] improves the performance but it is sensitive to the window size.…”

Section: A Proximity-base Information Retrieval Modelmentioning

confidence: 99%

A Novel Information Retrieval Approach using Query Expansion and Spectral-based

Alnofaie¹,

Dahab²,

Kamal³

2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…Rasolofo and Savoy (2003) added proximity information to the Okapi probabilistic model and found improved performance specifically among the top scored documents. Buttcher, Clarke, and Lushman (2006) also incorporated proximity into the Okapi BM25 model and observed positive results. Metzler and Croft (2005) introduced the use of the Markov random fields (MRF) for modeling term dependencies in the language-modeling framework.…”

Section: Related Workmentioning

confidence: 99%

Sentence-based relevance flow analysis for high accuracy retrieval

Lee

Seo

Jeon

et al. 2011

J. Am. Soc. Inf. Sci.

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Traditional ranking models for information retrieval lack the ability to make a clear distinction between relevant and nonrelevant documents at top ranks if both have similar bag-of-words representations with regard to a user query. We aim to go beyond the bag-of-words approach to document ranking in a new perspective, by representing each document as a sequence of sentences. We begin with an assumption that relevant documents are distinguishable from nonrelevant ones by sequential patterns of relevance degrees of sentences to a query. We introduce the notion of relevance flow, which refers to a stream of sentence-query relevance within a document. We then present a framework to learn a function for ranking documents effectively based on various features extracted from their relevance flows and leverage the output to enhance existing retrieval models. We validate the effectiveness of our approach by performing a number of retrieval experiments on three standard test collections, each comprising a different type of document: news articles, medical references, and blog posts. Experimental results demonstrate that the proposed approach can improve the retrieval performance at the top ranks significantly as compared with the state-of-the-art retrieval models regardless of document type.

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“…In this paper we aim to investigate the effectiveness of entity extraction from the texts of top retrieved documents. In our preliminary experiments we compared the extraction of entities from top documents retrieved from the ClueWeb09 Category B 1 collection using BM25tp (a variant of BM25 incorporating term proximity information) (Büttcher et al, 2006) with the extraction from top documents retrieved from the Web by a major search engine. The effectiveness of the latter approach is higher, and therefore it is used in the methods described in this paper.…”

Section: Extracting Candidate Entitiesmentioning

confidence: 99%

A domain-independent approach to finding related entities

Vechtomova

Robertson

2012

Information Processing & Management

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We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities.

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Term proximity scoring for ad-hoc retrieval on very large text collections

Cited by 126 publications

References 2 publications

A Novel Information Retrieval Approach using Query Expansion and Spectral-based

A Novel Information Retrieval Approach using Query Expansion and Spectral-based

Sentence-based relevance flow analysis for high accuracy retrieval

A domain-independent approach to finding related entities

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