AQtpUIR: Adaptive query term proximity based user information retrieval

2020 ◽  
Vol 41 (6) ◽  
pp. 1479-1497
Author(s):  
Tirthankar Barik ◽  
Vikram Singh
Keyword(s):  
Information Retrieval ◽  
Query Term

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.

Enhanced Spoken Sentence Retrieval Using a Conventional Automatic Speech Recognizer in Smart Home

2016 ◽  
Vol 25 (03) ◽  
pp. 1650017 ◽  
Author(s):  
Hyeokju Ahn ◽  
Harksoo Kim
Keyword(s):  
Information Retrieval ◽  
Smart Home ◽  
Retrieval System ◽  
Generation Model ◽  
Query Term ◽  
Post Processing ◽  
Retrieval Systems ◽  
Sentence Retrieval ◽  
Information Retrieval Systems ◽  
Performance Results

With the rapid evolution of smart home environment, the demand for spoken information retrieval (e.g., voice-activated FAQ retrieval) on information appliances is increasing. In spoken information retrieval, users’ spoken queries are converted into text queries using automatic speech recognition (ASR) engines. If top-1 results of the ASR engines are incorrect, the errors are propagated to information retrieval systems. If a document collection is a small set of sentences such as frequently asked questions (FAQs), the errors have additional effect on the performance of information retrieval systems. To improve the performance of such a sentence retrieval system, we propose a post-processing model of an ASR engine. The post-processing model consists of a re-ranking and a query term generation model. The re-ranking model rearranges top-n outputs of the ASR engines using the ranking support vector machine (Ranking SVM). The query term generation model extracts meaningful content words from the re-ranked queries based on term frequencies and query rankings. In the experiments, the re-ranking model improved the top-1 performance results of an underlying ASR engine with 4.4% higher precision and 6.4% higher recall rate. The query term generation model improved the performance results of an underlying information retrieval system with an accuracy 2.4% to 2.6% higher. Based on the experimental result, the proposed model revealed that it could improve the performance of a spoken sentence retrieval system in a restricted domain.

Analysis of Vector Space Method in Information Retrieval for Smart Answering System

2020 ◽  
Vol 17 (9) ◽  
pp. 4468-4472
Author(s):  
Deepa Yogish ◽  
T. N. Manjunath ◽  
Ravindra S. Hegadi
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Query Term ◽  
Frequency Method ◽  
Document Ranking ◽  
User Intent ◽  
Space Model ◽  
Relevant Document ◽  
User Query

In the world of internet, searching play a vital role to retrieve the relevant answers for the user specific queries. The most promising application of natural language processing and information retrieval system is Question answering system which provides directly the accurate answer instead of set of documents. The main objective of information retrieval is to retrieve relevant document from a huge volume of data sets underlying in the internet using appropriatemodel. There are many models proposed for retrieval process such as Boolean, Vector space and Probabilistic method. Vector space model is best method in information retrieval for document ranking with efficient document representation which combines simplicity and clarity. VSM adopts similarity function to measure the matching between documents and user intent, and assign scores from the biggest to smallest. The documents and query are assigned with weights using term frequency and inverse document frequency method. To retrieve most relevant document to the user query term, document ranking function cosine similarity score is applied for every document and user query. The documents having more similarity scores will be considered as relevant documents to the query term and they are ranked based on these scores. This paper emphasizes on different techniques of information retrieval and Vector Space Model offers a realistic compromise in IR processing. It allows best weighing scheme which ranks the set of documents in order of relevance based on user query.

Context Window Based Co-Occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval

2018 ◽  
pp. 1597-1613
Author(s):  
Jagendra Singh ◽  
Aditi Sharan
Keyword(s):  
Information Retrieval ◽  
Hybrid Method ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Contextual Information ◽  
Optimal Combination ◽  
Query Term ◽  
Benchmark Data ◽  
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.

scholarly journals A study on query terms proximity embedding for information retrieval

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771769489 ◽  
Author(s):  
Ya-nan Qiao ◽  
Qinghe Du ◽  
Di-fang Wan
Keyword(s):  
Wireless Networks ◽  
Information Retrieval ◽  
Cyber Physical Systems ◽  
Query Term ◽  
Retrieval Models ◽  
Physical Systems ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Original Information ◽  
Term Field

Information retrieval is applied widely to models and algorithms in wireless networks for cyber-physical systems. Query terms proximity has proved that it is a very useful information to improve the performance of information retrieval systems. Query terms proximity cannot retrieve documents independently, and it must be incorporated into original information retrieval models. This article proposes the concept of query term proximity embedding, which is a new method to incorporate query term proximity into original information retrieval models. Moreover, term-field-convolutions frequency framework, which is an implementation of query term proximity embedding, is proposed in this article, and experimental results show that this framework can improve the performance effectively compared with traditional proximity retrieval models.

Similarity Web Pages Retrieval Technologies on the Internet

2005 ◽  
pp. 2486-2491
Author(s):  
Rung Ching Chen ◽  
Ming Yung Tsai ◽  
Chung Hsun Hsieh
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Fast Growth ◽  
The Other ◽  
The Internet ◽  
Web Pages ◽  
Query Term ◽  
Web Page ◽  
Critical Problems

In recent years, due to the fast growth of the Internet, the services and information it provides are constantly expanding. Madria and Bhowmick (1999) and Baeza-Yates (2003) indicated that most large search engines need to comply to, on average, at least millions of hits daily in order to satisfy the users’ needs for information. Each search engine has its own sorting policy and the keyword format for the query term, but there are some critical problems. The searches may get more or less information. In the former, the user always gets buried in the information. Requiring only a little information, they always select some former items from the large amount of returned information. In the latter, the user always re-queries using another searching keyword to do searching work. The re-query operation also leads to retrieving information in a great amount, which leads to having a large amount of useless information. That is a bad cycle of information retrieval. The similarity Web page retrieval can help avoid browsing the useless information. The similarity Web page retrieval indicates a Web page, and then compares the page with the other Web pages from the searching results of search engines. The similarity Web page retrieval will allow users to save time by not browsing unrelated Web pages and reject non-similar Web pages, rank the similarity order of Web pages and cluster the similarity Web pages into the same classification.

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