scholarly journals Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Semantic Relations ◽  
Information Need ◽  
Weighted Tree ◽  
Average Precision ◽  
Arabic Information Retrieval ◽  
Semantic Tree ◽  
Expansion Model ◽  
Pseudo Relevance Feedback

In this paper, the authors propose and readapt a new concept-based approach of query expansion in the context of Arabic information retrieval. The purpose is to represent the query by a set of weighted concepts in order to identify better the user's information need. Firstly, concepts are extracted from the initially retrieved documents by the Pseudo-Relevance Feedback method, and then they are integrated into a semantic weighted tree in order to detect more information contained in the related concepts connected by semantic relations to the primary concepts. The authors use the “Arabic WordNet” as a resource to extract, disambiguate concepts and build the semantic tree. Experimental results demonstrate that measure of MAP (Mean Average Precision) is about 10% of improvement using the open source Lucene as IR System on a collection formed from the Arabic BBC news.

Word-embedding-based pseudo-relevance feedback for Arabic information retrieval

2018 ◽  
Vol 45 (4) ◽  
pp. 429-442 ◽  
Author(s):  
Abdelkader El Mahdaouy ◽  
Saïd Ouatik El Alaoui ◽  
Eric Gaussier
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Main Idea ◽  
Word Embedding ◽  
Average Precision ◽  
Standard Arabic ◽  
Arabic Information Retrieval ◽  
The Mean ◽  
Pseudo Relevance Feedback

Pseudo-relevance feedback (PRF) is a very effective query expansion approach, which reformulates queries by selecting expansion terms from top k pseudo-relevant documents. Although standard PRF models have been proven effective to deal with vocabulary mismatch between users’ queries and relevant documents, expansion terms are selected without considering their similarity to the original query terms. In this article, we propose a method to incorporate word embedding (WE) similarity into PRF models for Arabic information retrieval (IR). The main idea is to select expansion terms using their distribution in the set of top pseudo-relevant documents along with their similarity to the original query terms. Experiments are conducted on the standard Arabic TREC 2001/2002 collection using three neural WE models. The obtained results show that our PRF extensions significantly outperform their baseline PRF models. Moreover, they enhanced the baseline IR model by 22% and 68% for the mean average precision (MAP) and the robustness index (RI), respectively.

Context Window Based Co-occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval

2015 ◽  
Vol 5 (4) ◽  
pp. 31-45 ◽  
Author(s):  
Jagendra Singh ◽  
Aditi Sharan
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Contextual Information ◽  
Optimal Combination ◽  
Query Term ◽  
Benchmark Data ◽  
First Pass ◽  
Baseline Approach ◽  
Pseudo Relevance Feedback

Pseudo-relevance feedback (PRF) is a type of relevance feedback approach of query expansion that considers the top ranked retrieved documents as relevance feedback. In this paper the authors focus is to capture the limitation of co-occurrence and PRF based query expansion approach and the authors proposed a hybrid method to improve the performance of PRF based query expansion by combining query term co-occurrence and query terms contextual information based on corpus of top retrieved feedback documents in first pass. Firstly, the paper suggests top retrieved feedback documents based query term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, contextual window based approach is used to select the query context related terms from top feedback documents. Third, comparisons were made among baseline, co-occurrence and contextual window based approaches using different performance evaluating metrics. The experiments were performed on benchmark data and the results show significant improvement over baseline approach.

A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion

2016 ◽  
Vol 18 (6) ◽  
pp. 980-989 ◽  
Author(s):  
Jagendra Singh ◽  
Mukesh Prasad ◽  
Om Kumar Prasad ◽  
Er Meng Joo ◽  
Amit Kumar Saxena ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Logic Model ◽  
Fuzzy Logic Model ◽  
Pseudo Relevance Feedback

Query expansion using pseudo relevance feedback on wikipedia

2017 ◽  
Vol 50 (3) ◽  
pp. 455-478 ◽  
Author(s):  
Andisheh Keikha ◽  
Faezeh Ensan ◽  
Ebrahim Bagheri
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Pseudo Relevance Feedback

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

2019 ◽  
Vol 9 (6) ◽  
pp. 5016
Author(s):  
Siham Jabri ◽  
Azzeddine Dahbi ◽  
Taoufiq Gadi
Keyword(s):  
Association Rules ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Weighted Graph ◽  
Strong Correlations ◽  
Retrieval Performance ◽  
Dominance Relations ◽  
Baseline System ◽  
Text Query ◽  
Pseudo Relevance Feedback

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.

scholarly journals Improving the Results of Google Scholar Engine through Automatic Query Expansion Mechanism and Pseudo Re-ranking using MVRA

2018 ◽  
Vol 42 (2) ◽  
pp. 219-229
Author(s):  
Mawloud Mosbah
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Majority Voting ◽  
Google Scholar ◽  
Content Based Image Retrieval ◽  
Ranking Algorithm ◽  
Expansion Mechanism ◽  
Feedback Algorithm ◽  
Pseudo Relevance Feedback

In this paper, we address the enhancing of Google Scholar engine, in the context of text retrieval, through two mechanisms related to the interrogation protocol of that query expansion and reformulation. The both schemes are applied with re-ranking results using a pseudo relevance feedback algorithm that we have proposed previously in the context of Content based Image Retrieval (CBIR) namely Majority Voting Re-ranking Algorithm (MVRA). The experiments conducted using ten queries reveal very promising results in terms of effectiveness.

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