Bi-Gram Term Collocations-based Query Expansion Approach for Improving Arabic Information Retrieval

2018 ◽  
Vol 43 (12) ◽  
pp. 7705-7718 ◽  
Author(s):  
Ibrahim Moawad ◽  
Waseem Alromima ◽  
Rania Elgohary
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Arabic Information Retrieval

The Use of Arabic WordNet in Arabic Information Retrieval

2014 ◽  
Vol 4 (3) ◽  
pp. 54-65 ◽  
Author(s):  
Ahmed Abbache ◽  
Fatiha Barigou ◽  
Fatma Zohra Belkredim ◽  
Ghalem Belalem
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Appropriate Use ◽  
Average Precision ◽  
Linguistic Information ◽  
Interactive Query ◽  
Part Of Speech ◽  
Arabic Information Retrieval ◽  
Princeton Wordnet ◽  
The Impact

Research and experimentation using Arabic WordNet in the field of information retrieval are relatively new. It is limited compared to the research that has been done using Princeton WordNet. This work attempts to study the impact of Arabic WordNet on the performance of Arabic information retrieval. We extend Lucene with Arabic WordNet to expand user's queries. The major contribution of this study is to propose an interactive query expansion (IQE) methodology using the word's part-of-speech, according to the part it plays in a query. First, the user selects the appropriate part of speech for each term in the original query, and then he reselects the appropriate synonyms. Experimental results show that our IQE strategy produces a good Mean Average Precision (MAP), it is able to improve MAP by 12.6%, but no variant of automatic query expansion (AQE) strategies did. Nevertheless, the experiments allow us to conclude that with an appropriate use of Arabic WordNet as a source of linguistic information for AQE can improve effectiveness for Arabic information retrieval.

scholarly journals Semantic Query Expansion for Arabic Information Retrieval

2014 ◽  
Author(s):  
Ashraf Mahgoub ◽  
Mohsen Rashwan ◽  
Hazem Raafat ◽  
Mohamed Zahran ◽  
Magda Fayek
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Semantic Query ◽  
Arabic Information Retrieval

The Use of Arabic WordNet in Arabic Information Retrieval

2016 ◽  
pp. 773-783 ◽  
Author(s):  
Ahmed Abbache ◽  
Fatiha Barigou ◽  
Fatma Zohra Belkredim ◽  
Ghalem Belalem
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Mean Average Precision ◽  
Appropriate Use ◽  
Linguistic Information ◽  
Interactive Query ◽  
Part Of Speech ◽  
Arabic Information Retrieval ◽  
Princeton Wordnet ◽  
The Impact

Research and experimentation using Arabic WordNet in the field of information retrieval are relatively new. It is limited compared to the research that has been done using Princeton WordNet. This work attempts to study the impact of Arabic WordNet on the performance of Arabic information retrieval. We extend Lucene with Arabic WordNet to expand user's queries. The major contribution of this study is to propose an interactive query expansion (IQE) methodology using the word's part-of-speech, according to the part it plays in a query. First, the user selects the appropriate part of speech for each term in the original query, and then he reselects the appropriate synonyms. Experimental results show that our IQE strategy produces a good Mean Average Precision (MAP), it is able to improve MAP by 12.6%, but no variant of automatic query expansion (AQE) strategies did. Nevertheless, the experiments allow us to conclude that with an appropriate use of Arabic WordNet as a source of linguistic information for AQE can improve effectiveness for Arabic information retrieval.

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.

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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