Hybrid query expansion model for text and microblog information retrieval

2018 ◽  
Vol 21 (4) ◽  
pp. 337-367 ◽  
Author(s):  
Meriem Amina Zingla ◽  
Chiraz Latiri ◽  
Philippe Mulhem ◽  
Catherine Berrut ◽  
Yahya Slimani
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Expansion Model

An Information Retrieval Expansion Model Based on Wikipedia

2014 ◽  
Vol 977 ◽  
pp. 464-467
Author(s):  
Li Xin Gan ◽  
Wei Tu
Keyword(s):  
Information Retrieval ◽  
Classification Accuracy ◽  
Query Expansion ◽  
Semantic Network ◽  
Computational Cost ◽  
Retrieval Performance ◽  
Network Information ◽  
Key Technologies ◽  
Query Classification ◽  
Expansion Model

Query expansion is one of the key technologies for improving precision and recall in information retrieval. In order to overcome limitations of single corpus, in this paper, semantic characteristics of Wikipedia corpus is combined with the standard corpus to extract more rich relationship between terms for construction of a steady Markov semantic network. Information of the entity pages and disambiguation pages in Wikipedia is comprehensively utilized to classify query terms to improve query classification accuracy. Related candidates with high quality can be used for query expansion according to semantic pruning. The proposal in our work is benefit to improve retrieval performance and to save search computational cost.

Query expansion model based on concept graph information retrieval

2009 ◽  
Vol 29 (2) ◽  
pp. 545-548 ◽  
Author(s):  
Rui CHEN ◽  
Lei ZHANG ◽  
Chun-jun LU ◽  
Li-ke MOU
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Model Based ◽  
Expansion Model

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

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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