Jagendra Singh

Relevance feedback based query expansion model using Borda count and semantic similarity approach

Pseudo-Relevance Feedback (PRF) is a well-known method of query expansion for improving the performance of information retrieval systems. All the terms of PRF documents are not important for expanding the user query. Therefore selection of proper expansion term is very important for improving system performance. Individual query expansion terms selection methods have been widely investigated for improving its performance. Every individual expansion term selection method has its own weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, first the possibility of improving the overall performance using individual query expansion terms selection methods has been explored. Second, Borda count rank aggregation approach is used for combining multiple query expansion terms selection methods. Third, the semantic similarity approach is used to select semantically similar terms with the query after applying Borda count ranks combining approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over individual terms selection method and related state-of-the-art methods.

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

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 new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach

Abstract Efficient query expansion (QE) terms selection methods are really very important for improving the accuracy and efficiency of the system by removing the irrelevant and redundant terms from the top-retrieved feedback documents corpus with respect to a user query. Each individual QE term selection method has its weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, we present a new method for QE based on fuzzy logic considering the top-retrieved document as relevance feedback documents for mining additional QE terms. Different QE terms selection methods calculate the degrees of importance of all unique terms of top-retrieved documents collection for mining additional expansion terms. These methods give different relevance scores for each term. The proposed method combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Then, the weights of the additional query terms and the weights of the original query terms are used to form the new query vector, and we use this new query vector to retrieve documents. All the experiments are performed on TREC and FIRE benchmark datasets. The proposed QE method increases the precision rates and the recall rates of information retrieval systems for dealing with document retrieval. It gets a significant higher average recall rate, average precision rate and F measure on both datasets.

Co-occurrence and Semantic Similarity Based Hybrid Approach for Improving Automatic Query Expansion in Information Retrieval

Pseudo Relevance feedback PRF based query expansion approaches assumes that the top ranked retrieved documents are relevant. But this assumption is not always true; it may also possible that a PRF document may contain different topics, which may or may not be relevant to the query terms even if the documents are judged relevant. In this paper our focus is to capture the limitation of PRF based query expansion and propose a hybrid method to improve the performance of PRF based query expansion by combining corpus based term co-occurrence information and semantic information of term. Firstly, the paper suggest use of corpus based term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, we use semantic similarity approach to rank the query expansion terms obtained from top feedback documents. Third, we combine co-occurrence and semantic similarity together to rank the query expansion terms obtained from first step on the basis of semantic similarity. The experiments were performed on FIRE ad hoc and TREC-3 benchmark datasets of information retrieval. The results show significant improvement in terms of precision, recall and mean average precision MAP. This experiments shows that the combination of both techniques in an intelligent way gives us goodness of both of them. As this is the first attempt in this direction there is a large scope of improving these techniques.

A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion

In this paper, a novel fuzzy logic-based expansion approach considering the relevance score produced by different rank aggregation approaches is proposed. It is well known that different rank aggregation approaches yield different relevance scores for each term. The proposed fuzzy logic approach combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Experimental results demonstrate that the proposed approach achieves significant improvement over individual expansion, aggregated and other related state-of-the-arts methods.

Relevance Feedback-based Query Expansion Model using Ranks Combining and Word2Vec Approach

ABSTRACT Query expansion is a well-known method for improving the performance of information retrieval systems. Pseudo-relevance feedback (PRF)-based query expansion is a type of query expansion approach that assumes the top-ranked retrieved documents are relevant. The addition of all the terms of PRF documents is not important or appropriate for expanding the original user query. Hence, the selection of proper expansion term is very important for improving retrieval system performance. Various individual query expansion term selection methods have been widely investigated for improving system performance. Every individual expansion term selection method has its own weaknesses and strengths. In order to minimize the weaknesses and utilizing the strengths of the individual method, we used multiple terms selection methods together. First, this paper explored the possibility of improving overall system performance by using individual query expansion terms selection methods. Further, ranks-aggregating method named Borda count is used for combining multiple query expansion terms selection methods. Finally, Word2vec approach is used to select semantically similar terms with query after applying Borda count rank combining approach. Our experimental results on both data-sets TREC and FIRE demonstrated that our proposed approaches achieved significant improvement over each individual terms selection method and others related state-of-the-art method.

Lexical Ontology-Based Computational Model to Find Semantic Similarity

Finding semantic similarity between two words or concepts has been considered as a challenging task in the field of natural language processing. Some lexical ontology-based approaches have been developed for this purpose. However, these approaches have been tested only for English language. Based on survey, there is no computational model for calculating semantic similarity between Hindi concepts. We cannot ignore Hindi language, because it is the third most spoken language of the world. In this paper, we present a computational model for calculating semantic similarity between words/concepts with the help of lexical ontology, which has been tested for Hindi language. Further, experiments have been carried out on a benchmark data set translated from English to Hindi. In our proposed computational model, Hindi WordNet has been used to get relational information between Hindi concepts. Existing popular semantic similarity approaches have been used to calculate semantic similarity. Miller and Charles’s benchmark data set was used to evaluate our proposed approach. We calculated the semantic similarity between 20 word pairs by using three different semantic similarity measures. Accuracy of the results was measured by calculating correlation coefficient between these similarity measures and human judgment. Our proposed model is useful in following ways. Firstly, it allows us to study and analyze the results of available semantic similarity methods on Hindi words. Secondly, it provides a general module along with algorithms, which can be tuned to develop similar modules for any other language.

Rank fusion and semantic genetic notion based automatic query expansion model

Abstract Query expansion term selection methods are really very important for improving the accuracy and efficiency of pseudo-relevance feedback based automatic query expansion for information retrieval system by removing irrelevant and redundant terms from the top retrieved feedback documents corpus with respect to user query. Individual query expansion term selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual query expansion term selection method that would outperform other individual query expansion term selection methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual query expansion term selection methods. Second, we propose a model for combining multiple query expansion term selection methods by using rank combination approach, called multiple ranks combination based query expansion. Third, semantic filtering is used to filter semantically irrelevant term obtained after combining multiple query expansion term selection methods, called ranks combination and semantic filtering based query expansion. Fourth, the genetic algorithm is used to make an optimal combination of query terms and candidate term obtained after rank combination and semantic filtering approach, called semantic genetic filtering and rank combination based query expansion. Our experimental results demonstrated that our proposed approaches achieved significant improvement over each individual query expansion term selection method and related state-of-the-art approaches.

Term co-occurrence and context window-based combined approach for query expansion with the semantic notion of terms

In this paper, our focus is to capture the limitations of Pseudo-Relevance Feedback (PRF) based query expansion (QE) and propose a hybrid method to improve the performance of PRF-based QE by combining corpus-based term co-occurrence information, context window of query terms and semantic information of term. Firstly, the paper suggests use of various corpus-based term co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using PRF-based QE. Third, we use semantic similarity approach to rank the QE terms obtained from top feedback documents. Fourth, we combine co-occurrence, context window and semantic similarity based approaches together to select the best expansion for query reformulation. The experiments were performed on FIRE ad-hoc and TREC-3 benchmark datasets of information retrieval task. The results show significant improvement in terms of precision, recall and mean average precision (MAP). This experiment shows that the combination of various techniques in an intelligent way gives us goodness of all of them.

Keyword Extraction from Hindi Documents Using Statistical Approach

Keywords of a document give us an idea about its important points without going through the whole text. In this paper, we propose an unsupervised, domain-independent, and corpus-independent approach for automatic keyword extraction. The approach is general and can be applied to any language. However, we have tested the approach on Hindi language. Our approach combines the information contained in frequency and spatial distribution of a word in order to extract keywords from a document. Our work is specially significant in the light that it has been implemented and tested on Hindi which is a resource poor and underrepresented language.