Iterative Estimation of Document Relevance Score for Pseudo-Relevance Feedback

Ariannezhad, Mozhdeh; Montazeralghaem, Ali; Zamani, Hamed; Shakery, Azadeh

doi:10.1007/978-3-319-56608-5_65

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Pseudorelevance feedback is a well-known model of automatic query expansion, and has shown improvements in the performance of retrieval. Several models for pseudorelevance feedback has been developed [2,20,[24][25][26]31].…”

Section: Semantic Matchingmentioning

confidence: 99%

Relevance Ranking Based on Query-Aware Context Analysis

Montazeralghaem

Rahimi

Allan

2020

Lecture Notes in Computer Science

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Word mismatch between queries and documents is a longstanding challenge in information retrieval. Recent advances in distributed word representations address the word mismatch problem by enabling semantic matching. However, most existing models rank documents based on semantic matching between query and document terms without an explicit understanding of the relationship of the match to relevance. To consider semantic matching between query and document, we propose an unsupervised semantic matching model by simulating a user who makes relevance decisions. The primary goal of the proposed model is to combine the exact and semantic matching between query and document terms, which has been shown to produce effective performance in information retrieval. As semantic matching between queries and entire documents is computationally expensive, we propose to use local contexts of query terms in documents for semantic matching. Matching with smaller query-related contexts of documents stems from the relevance judgment process recorded by human observers. The most relevant part of a document is then recognized and used to rank documents with respect to the query. Experimental results on several representative retrieval models and standard datasets show that our proposed semantic matching model significantly outperforms competitive baselines in all measures.

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Section: Semantic Matchingmentioning

confidence: 99%

Relevance Ranking Based on Query-Aware Context Analysis

Montazeralghaem

Rahimi

Allan

2020

Lecture Notes in Computer Science

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“…As Xu & Croft (1996) confirmed in their experiments, an expansion with local feedback based on the search results of the original query is more suitable than global techniques that examine word relationships in a corpus. Therefore, the application examples in this paper use an expansion with local feedback called Pseudo Relevance Feedback (PRF), as this technique currently represents the state of the art (Ariannezhad, Montazeralghaem, Zamani, & Shakery, 2017;Keikha, Ensan, & Bagheri, 2017).…”

Section: Related Workmentioning

confidence: 99%

Augmenting a Research Information System with automatically acquired category and keyword information

Blanck¹,

Niekler²,

Kaulisch³

2020

Preprint

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With an ever-increasing amount of data, it is essential for many systems that documents can be retrieved efficiently.The process of information retrieval can be supported by metadata enrichment of the documents. The aim of thiswork is to make scientific publications and project descriptions, consisting of titles, abstracts and bibliographicalreferences, easier to find. Therefore, we investigate text analytical methods such as keyword extraction algorithms(TFIDF, Log-Likelihood, RAKE, TAKE and KECNW) and classification approaches using a SVM withensembles of classifier chains (Web of Science and GEPRIS categories as taxonomies) and compare their quality.We present an altered an optimized keyword extraction algorithm and a supervised subject and keywordclassification approach which are, to our knowledge so far, one of the first automatic applications of this kind ininformetrics and scientific information retrieval.The most promising methods are employed and the extracted information is attached to the documents as metadata.These support a search query, using pseudo relevance feedback, to obtain further relevant search results and canalso be used to derive profiles for authors, faculties, etc. The concepts developed here will serve as a basis for theLeipzig University Research Information System.

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“…However, if the documents used for this relevance feedback are irrelevant, the selected expansion terms impact the retrieval performance negatively [13]. Ariannezhad et al [14] proposed a new approach which consider that the documents containing more informative terms for PRF should have higher relevance scores. Moreover, an iterative algorithm is provided for ensuring the satisfaction of the proposed constraint for any PRF model.…”

mentioning

confidence: 99%

A Graph-based approach for text query expansion using pseudo relevance feedback and association rules mining

Jabri¹,

Dahbi²,

Gadi³

2019

IJECE

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Pseudo-relevance feedback is a query expansion approach whose terms are selected from a set of top ranked retrieved documents in response to the original query. However, the selected terms will not be related to the query if the top retrieved documents are irrelevant. As a result, retrieval performance for the expanded query is not improved, compared to the original one. This paper suggests the use of documents selected using Pseudo Relevance Feedback for generating association rules. Thus, an algorithm based on dominance relations is applied. Then the strong correlations between query and other terms are detected, and an oriented and weighted graph called Pseudo-Graph Feedback is constructed. This graph serves for expanding original queries by terms related semantically and selected by the user. The results of the experiments on Text Retrieval Conference (TREC) collection are very significant, and best results are achieved by the proposed approach compared to both the baseline system and an existing technique.

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Iterative Estimation of Document Relevance Score for Pseudo-Relevance Feedback

Cited by 6 publications

References 12 publications

Relevance Ranking Based on Query-Aware Context Analysis

Relevance Ranking Based on Query-Aware Context Analysis

Augmenting a Research Information System with automatically acquired category and keyword information

A Graph-based approach for text query expansion using pseudo relevance feedback and association rules mining

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