Query Expansion for Sentence Retrieval Using Pseudo Relevance Feedback and Word Embedding

2017 ◽  
pp. 97-103 ◽  
Author(s):  
Piyush Arora ◽  
Jennifer Foster ◽  
Gareth J. F. Jones
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Word Embedding ◽  
Sentence Retrieval ◽  
Pseudo Relevance Feedback

scholarly journals Query Expansion menggunakan Word Embedding dan Pseudo Relevance Feedback

2019 ◽  
Vol 5 (1) ◽  
pp. 47 ◽  
Author(s):  
Evan Tanuwijaya ◽  
Safri Adam ◽  
Mohammad Fatoni Anggris ◽  
Agus Zainal Arifin
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Relevant Information ◽  
Word Embedding ◽  
Natural Languages ◽  
F Measure ◽  
Pseudo Relevance Feedback ◽  
Better Than

Kata kunci merupakan hal terpenting dalam mencari sebuah informasi. Penggunaan kata kunci yang tepat menghasilkan informasi yang relevan. Saat penggunaannya sebagai query, pengguna menggunakan bahasa yang alami, sehingga terdapat kata di luar dokumen jawaban yang telah disiapkan oleh sistem. Sistem tidak dapat memproses bahasa alami secara langsung yang dimasukkan oleh pengguna, sehingga diperlukan proses untuk mengolah kata-kata tersebut dengan mengekspansi setiap kata yang dimasukkan pengguna yang dikenal dengan Query Expansion (QE). Metode QE pada penelitian ini menggunakan Word Embedding karena hasil dari Word Embedding dapat memberikan kata-kata yang sering muncul bersama dengan kata-kata dalam query. Hasil dari word embedding dipakai sebagai masukan pada pseudo relevance feedback untuk diperkaya berdasarkan dokumen jawaban yang telah ada. Metode QE diterapkan dan diuji coba pada aplikasi chatbot. Hasil dari uji coba metode QE yang diterapkan pada chatbot didapatkan nilai recall, precision, dan F-measure masing-masing 100%; 70% dan 82,35 %. Hasil tersebut meningkat 1,49% daripada chatbot tanpa menggunakan QE yang pernah dilakukan sebelumnya yang hanya meraih akurasi sebesar 68,51%. Berdasarkan hasil pengukuran tersebut, QE menggunakan word embedding dan pseudo relevance feedback pada chatbot dapat mengatasi query masukan dari pengguna yang ambigu dan alami, sehingga dapat memberikan jawaban yang relevan kepada pengguna.  Keywords are the most important words and phrases used to obtain relevant information on content. Although users make use of natural languages, keywords are processed as queries by the system due to its inability to process. The language directly entered by the user is known as query expansion (QE). The proposed QE in this research uses word embedding owing to its ability to provide words that often appear along with those in the query. The results are used as inputs to the pseudo relevance feedback to be enriched based on the existing documents. This method is also applied to the chatbot application and precision, and F-measure values of the results obtained were 100%, 70%, 82.35% respectively. The results are 1.49% better than chatbot without using QE with 68.51% accuracy. Based on the results of these measurements, QE using word embedding and pseudo which gave relevance feedback in chatbots can resolve ambiguous and natural user’s input queries thereby enabling the system retrieve relevant answers.

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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