scholarly journals Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

2022 ◽  
Vol 71 (2) ◽  
pp. 3557-3570
Author(s):  
Abhishek Kumar Shukla ◽  
Sujoy Das
Keyword(s):  
Neural Network ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Deep Neural Network ◽  
Pseudo Relevance Feedback

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.

scholarly journals Semantic expansion of search queries

2021 ◽  
Author(s):  
Andisheh Keikha
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Web Search ◽  
State Of The Art ◽  
Feature Learning ◽  
Correct Interpretation ◽  
Search Queries ◽  
Term Selection ◽  
Semantic Expansion ◽  
Pseudo Relevance Feedback

One of the major challenges in Web search pertains to the correct interpretation of users' intent. Query Expansion is one of the well-known approaches for determining the intent of the user by addressing the vocabulary mismatch problem. A limitation of the current query expansion approaches is that the relations between the query words and the expanded words is limited. In this thesis, we capture users' intent through query expansion. We build on earlier work in the area by adopting a pseudo-relevance feedback approach; however, we advance the state of the art by proposing an approach for feature learning within the process of query expansion. In our work, we specifically consider the Wikipedia corpus as the feedback collection space and identify the best features within this context for term selection in two supervised and unsupervised models. We compare our work with state of the art query expansion techniques, the results of which show promising robustness and improved precision.

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