A Clustering Approach to Improving Pseudo-Relevance Feedback: Improving Retrieval Effetiveness by Removing Noisy Documents

2012 ◽  
Author(s):  
Changchun Li ◽  
Jun-yi Wang
Keyword(s):  
Relevance Feedback ◽  
Clustering Approach ◽  
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.

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