Query Expansion Terms Research Articles

A Study of Word Bigrams for Pseudo-relevance Feedback in Information Retrieval

Traditional information retrieval models mostly adopt a term independence assumption and are based on single terms or unigrams. Past efforts have attempted to go beyond this assumption, such as by using contiguous terms (i.e. word n-grams) or terms appearing in proximity. One such approach employs pseudo-relevance feedback (PRF) in an extended BM25 model, with an expanded query containing bigrams and proximity word pairs besides unigrams. However, the benefit of this approach over the traditional unigram PRF remains inconclusive. We speculate the uncertain effectiveness of bigram PRF in this past work is due to: (1) The new bigrams obtained from the expanded query may be formed by pairing unigrams drawn from different documents. These are potentially noise instead of relevant concepts; (2) The collection statistics of n-grams needed to calculate the document ranking functions, such as their document frequency, is not available in retrieval. Only estimates of these quantities are used instead. We suggest that these issues may be overcome by extracting word n-grams as single units in query expansion, and employing a document index that contains both unigrams and word n-grams. We demonstrate the approach for the case of bigram PRF in an extended BM25. Retrieval experiments are conducted on a range of standard test collections. For the majority of tested collections, the difference between values of the evaluation metrics (Mean Average Precision and the precision-oriented NDCG@20) obtained by our bigram PRF and the unigram PRF baseline is not statistically significant. Thus, our bigram PRF fails to improve over unigram PRF robustly across collections. An analysis of our results reveals ‘query drift’ due to bigram query expansion terms that represent too-broad topics as a cause for the failure of our approach.

Query Expansion Using Proposed Location-Based Algorithm for Hindi–English CLIR: Analyzing Three Test Collections

The rapid growth of contents on the Web in different languages increases the demand of Cross-Lingual Information Retrieval (CLIR). The accuracy of result suffers due to many problems such as ambiguity and drift issue in query. Query Expansion (QE) offers reliable solution for obtaining suitable documents for user queries. In this paper, we proposed an architecture for Hindi–English CLIR system using QE for improving the relevancy of retrieved results. In this architecture, for the addition of term(s) at appropriate position(s), we proposed a location-based algorithm to resolve the drift query issue in QE. User queries in Hindi language have been translated into document language (i.e. English) and the accuracy of translation is improved using Back-Translation. Google search has been performed and the retrieved documents are ranked using Okapi BM25 to arrange the documents in the order of decreasing relevancy to select the most suitable terms for QE. We used term selection value (TSV) for QE and for retrieving the terms, we created three test collections namely the (i) description and narration of the Forum for Information Retrieval Evaluation (FIRE) dataset, (ii) Snippets of retrieved documents against each query and (iii) Nearest-Neighborhood (NN) words against each query word among the ranked documents. To evaluate the system, 50 queries of Hindi language are selected from the FIRE-2012 dataset. In this paper, we performed two experiments: (i) impact of the proposed location-based algorithm on the proposed architecture of CLIR; and (ii) analysis of QE using three datasets, i.e. FIRE, NN and Snippets. In the first case, result shows that the relevancy of Hindi–English CLIR is improved by performing QE using the location-based algorithm and a 12% of improvement is achieved as compared to the results of QE obtained without applying the location-based algorithm. In the second case, the location-based algorithm is applied on three datasets. The Mean Average Precision (MAP) values of retrieved documents after QE are 0.5379 (NN), 0.6018 (FIRE) and 0.6406 (Snippets) for the three test collections, whereas the MAP before QE is 0.37102. This clearly shows the significant improvement of retrieved results for all three test collections. Among the three test collections, QE has been found most effective along with Snippets as indicated by the results with the improvements of 6.48% and 19.12% over FIRE and NN test collections, respectively.

Query Context Expansion for Open-Domain Question Answering

Humans are accustomed to autonomously associating prior knowledge with the text in a query when answering questions. However, for machines lacking cognition and common sense, a query is merely a combination of some words. Although we can enrich the semantic information of the given query through language representation or query expansion (QE) , the information contained in the query is still insufficient. In this paper, we propose an effective passage retrieval method named query context expansion-based retrieval (QCER) for open-domain question answering (OpenQA) . QCER associates a query with domain information by adding contextual association information based on the pseudo-relevance feedback (PRF) . QCER uses a dense reader to select top-n expansion terms for QE. We implement QCER by appending reader predictions, theoretically present in candidate passages, as contextual information to the initial query to form the new query. QCER with sparse representations (BM25) can improve retrieval efficiency and accelerate query convergence so that the reader can find the desired answer using fewer relevant passages, e.g., 10 passages, as soon as possible. Moreover, QCER can be easily combined with dense passage retrieval (DPR) to achieve even better performance, as sparse and dense representations are often complementary. Remarkably, we demonstrate that QCER achieves state-of-the-art performance in three tasks, passage retrieval, passage reading, and passage reranking, on the Natural Questions (NQ) and TriviaQA (Trivia) datasets under an extractive QA setup.

SPRF: A semantic Pseudo-relevance Feedback enhancement for information retrieval via ConceptNet

Pseudo-relevance feedback is a widely acclaimed technique for information retrieval. However, traditional information retrieval approaches typically process the original query into individual terms, often overlooking the consideration of the semantic information in the query term itself, by instead focusing on features such as term frequency and inverse document frequency. In this paper, we propose a new semantic-based Pseudo-relevance Feedback model (SPRF) based on the PRF framework. Our SPRF model leverages ConceptNet to provide comprehensive semantic information between terms. It not only considers the query’s importance in the collection but also integrates its semantic information into the PRF framework to enhance the selection of query expansion terms, leading to more precise feedback documents for users. A series of experimental results show that our proposed SPRF model is feasible. Our model achieves good performance in terms of the MAP, P@10, NDCG and MRR metrics and demonstrates advantages over the baseline models, the state-of-the-art models and several neural network-based methods. By comparing and analyzing the methods in a sample case, the extended terms resulting from the proposed model are shown to be in better semantic agreement with the given query.

Learning to rank query expansion terms for COVID-19 scholarly search

Objective:With the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, there has been a surge in the number of publicly available biomedical information sources, which makes it an increasingly challenging research goal to retrieve a relevant text to a topic of interest. In this paper, we propose a Contextual Query Expansion framework based on the clinical Domain knowledge (CQED) for formalizing an effective search over PubMed to retrieve relevant COVID-19 scholarly articles to a given information need. Materials and Methods:For the sake of training and evaluation, we use the widely adopted TREC-COVID benchmark. Given a query, the proposed framework utilizes a contextual and a domain-specific neural language model to generate a set of candidate query expansion terms that enrich the original query. Moreover, the framework includes a multi-head attention mechanism that is trained alongside a learning-to-rank model for re-ranking the list of generated expansion candidate terms. The original query and the top-ranked expansion terms are posed to the PubMed search engine for retrieving relevant scholarly articles to an information need. The framework, CQED, can have four different variations, depending upon the learning path adopted for training and re-ranking the candidate expansion terms. Results:The model drastically improves the search performance, when compared to the original query. The performance improvement in comparison to the original query, in terms of RECALL@1000 is 190.85% and in terms of NDCG@1000 is 343.55%. Additionally, the model outperforms all existing state-of-the-art baselines. In terms of P@10, the model that has been optimized based on Precision outperforms all baselines (0.7987). On the other hand, in terms of NDCG@10 (0.7986), MAP (0.3450) and bpref (0.4900), the CQED model that has been optimized based on an average of all retrieval measures outperforms all the baselines. Conclusion:The proposed model successfully expands queries posed to PubMed, and improves search performance, as compared to all existing baselines. A success/failure analysis shows that the model improved the search performance of each of the evaluated queries. Moreover, an ablation study depicted that if ranking of generated candidate terms is not conducted, the overall performance decreases. For future work, we would like to explore the application of the presented query expansion framework in conducting technology-assisted Systematic Literature Reviews (SLR).

Optimal Query Expansion Based on Hybrid Group Mean Enhanced Chimp Optimization Using Iterative Deep Learning

The internet is surrounded by uncertain information which necessitates the usage of natural language processing and soft computing techniques to extract the relevant documents. The relevant results are retrieved using the query expansion technique which is mainly formulated using the machine learning or deep learning concepts in the existing literature. This paper presents a hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for pseudo-relevance-based query expansion, whereby the actual queries are expanded with their related keywords. The hybrid GMBO-ECO algorithm mainly expands the query based on the terms that have a strong interrelationship with the actual query. To generate the word embeddings, a Word2Vec paradigm is used which learns the word association from large text corpora. The useful context in the text is identified using the improved iterative deep learning framework which determines the user’s intent for the current web search. This step reduces the mismatch of the words and improves the performance of query retrieval. The weak terms are eliminated and the candidate query terms for optimal query expansion are improved via an Okapi measure and cosine similarity techniques. The proposed methodology has been compared to the state-of-the-art methods with and without a query expansion approach. Moreover, the proposed optimal query expansion technique has shown a substantial improvement in terms of a normalized discounted cumulative gain of 0.87, a mean average precision of 0.35, and a mean reciprocal rank of 0.95. The experimental results show the efficiency of the proposed methodology in retrieving the appropriate response for information retrieval. The most common applications for the proposed method are search engines.

Document Representation and Query Expansion Models for Blog Recommendation

We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad-hocinformation retrieval in several ways: (1) the unit of output (the blog) is composed of a collection of documents (the blog posts) rather than a single document, (2) the query represents an ongoing and typically multifaceted interest in the topic rather than a passing ad-hoc information need and (3) due to the propensity of spam, splogs, and tangential comments, the blogosphere is particularly challenging to use as a source for high-quality query expansion terms. We address these differences at the document representation level, by comparing retrieval models that view either the blog or its constituent posts as the atomic units of retrieval, and at the query expansion level, by making novel use of the links and anchor text in Wikipedia1 to expand a user's initial query. We develop two complementary models of blog retrieval that perform at comparable levels of precision and recall. We also show consistent and significant improvement across all models using our Wikipedia expansion strategy.

Health Query Expansion based on Graph Matching between DBpedia and UMLS

<p class="0abstract">Information Retrieval (IR) in the medical domain is considered as a challenging task for many reasons. Short health queries tend to lack information on user's intent, and the target corpus may not have sufficient information for Relevance Feedbacks. And even, if the user obtains relevant documents to his/her queries, it is difficult for him/her to understand the technical terms. In contrast, in this paper, we propose an approach for health queries reformulation based on graph matching between two external linked data sources: DBpedia and Unified Medical Language System (UMLS). DBpedia has a broad coverage of topics and less noise compared to Wikipedia articles, and UMLS is specific to the medical domain. We also introduced the degree centrality to measure the graph connectivity and to select the most efficient candidate terms for query expansion. Experimental results on MEDLINE collection using Okapi BM25 as a retrieval model showed that our approach outperformed related methods, and the two sources achieved very good retrieval results. They helped in the diversification of the retrieved documents and the improvement of the recall.</p>

Research on Query Term Expansion based on RankSVM and LDA Model

The lack of semantic association will lead to poor performance of retrieval system. This paper proposed a query terms extension method based on RankSVM and LDA model. Firstly, with the aid of RankSVM’s excellent performance of sorting, high-quality initial retrieved results will be obtained. Then utilizing the LDA model to select the documents related to query term and to generate topic model for sequenced initial retrieval results.in the post-processing step, using threshold to select the highest probability among each topic and regards them as query terms set. Experiments show that the query terms expansion method proposed in this paper is superior to the current query expansion methods based on pseudo-relevance feedback in the five evaluation index, therefore this is a feasible method.

A contemporary combined approach for query expansion

The use of an automatic query expansion technique is to enhance the performance of the Information Retrieval System. Selecting the candidate terms for query expansion is an essential task to make query more precise to extract the most suitable documents. This paper provides a method to select the best terms for query enhancement. Firstly, the effect of abbreviation resolution, Lexical Variation, Synonyms, n-gram pseudo-relevance feedback, Co-occurrence method on baseline approaches of query expansion is analyzed.. In this work, we used the Okapi BM25 algorithm for ranking. We used Concept-based normalization to deal with concept terms. Here our results show the improvement in results than the baseline approach. A new combined technique that integrates lexical variation, synonyms, n-gram pseudo relevance feedback for query enhancement is proposed. For experimental purpose three English written datasets CACM, CISI, and TREC-3 is used. The obtained results show improvement in the performance of query expansion concerning mean average precision, F-measure, and precision-recall curve.

A hybrid semantic query expansion approach for Arabic information retrieval

In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have similar meaning with syntactical different keywords (form). One of the well-known approaches to overcome this limitation is query expansion (QE). There are several approaches in query expansion field such as statistical approach. This approach depends on term frequency to generate expansion features; nevertheless it does not consider meaning or term dependency. In addition, there are other approaches such as semantic approach which depends on a knowledge base that has a limited number of terms and relations. In this paper, researchers propose a hybrid approach for query expansion which utilizes both statistical and semantic approach. To select the optimal terms for query expansion, researchers propose an effective weighting method based on particle swarm optimization (PSO). A system prototype was implemented as a proof-of-concept, and its accuracy was evaluated. The experimental was carried out based on real dataset. The experimental results confirm that the proposed approach enhances the accuracy of query expansion.

Document-based and term-based linear methods for pseudo-relevance feedback

Query expansion is a successful approach for improving Information Retrieval effectiveness. This work focuses on pseudo-relevance feedback (PRF) which provides an automatic method for expanding queries without explicit user feedback. These techniques perform an initial retrieval with the original query and select expansion terms from the top retrieved documents. We propose two linear methods for pseudo-relevance feedback, one document-based and another term-based, that models the PRF task as a matrix decomposition problem. These factorizations involve the computation of an inter-document or inter-term similarity matrix which is used for expanding the original query. These decompositions can be computed by solving a least squares regression problem with regularization and a non-negativity constraint. We evaluate our proposals on five collections against state-of-the-art baselines. We found that the term-based formulation provides high figures of MAP, nDCG and robustness index whereas the document-based formulation provides very cheap computation at the cost of a slight decrease in effectiveness.

QER: a new feature selection method for sentiment analysis

Sentiment analysis is about the classification of sentiments expressed in review documents. In order to improve the classification accuracy, feature selection methods are often used to rank features so that non-informative and noisy features with low ranks can be removed. In this study, we propose a new feature selection method, called query expansion ranking, which is based on query expansion term weighting methods from the field of information retrieval. We compare our proposed method with other widely used feature selection methods, including Chi square, information gain, document frequency difference, and optimal orthogonal centroid, using four classifiers: naïve Bayes multinomial, support vector machines, maximum entropy modelling, and decision trees. We test them on movie and multiple kinds of product reviews for both Turkish and English languages so that we can show their performances for different domains, languages, and classifiers. We observe that our proposed method achieves consistently better performance than other feature selection methods, and query expansion ranking, Chi square, information gain, document frequency difference methods tend to produce better results for both the English and Turkish reviews when tested using naïve Bayes multinomial classifier.

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.

Lexical Co-Occurrence and Contextual Window-Based Approach with Semantic Similarity for Query Expansion

Query expansion (QE) is an efficient method for enhancing the efficiency of information retrieval system. In this work, we try to capture the limitations of pseudo-feedback based QE approach and propose a hybrid approach for enhancing the efficiency of feedback based QE by combining corpus-based, contextual based information of query terms, and semantic based knowledge of query terms. First of all, this paper explores the use of different corpus-based lexical co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using pseudo-feedback based QE. Next, we explore semantic similarity approach based on word2vec for ranking the QE terms obtained from top pseudo-feedback documents. Further, we combine co-occurrence statistics, contextual window statistics, and semantic similarity based approaches together to select the best expansion terms for query reformulation. The experiments were performed on FIRE ad-hoc and TREC-3 benchmark datasets. The statistics of our proposed experimental results show significant improvement over baseline method.

An Optimal Ranking Approach for Cluster based of Clicked URLsusing Firefly Algorithm for Efficient Personalized Web Search

Research has been finished based on query development to link the gap between terminology of user search query and documents related to user in order to recover appropriate documents before time in search results. In this paper novel approach is proposed which utilizes firefly algorithm for creating the cluster based optimal ranked clicked URLs for efficient personalized web search. The key input query issued for search on the web is used to choose the cluster for recommending the set of terms for query expansion. The process of retrieval of web search results using input query expanded with the selected terms along with the recommendations of next set of query terms for query expansion continues till the search is personalized by the data need of the user. The outcomes exhibit that our proposed work is greatly improved than the existing work.

Leveraging semantic resources in diversified query expansion

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. In this paper, we consider the usage of semantic resources and tools to arrive at improved methods for diversified query expansion. In particular, we develop two methods, those that leverage Wikipedia and pre-learnt distributional word embeddings respectively. Both the approaches operate on a common three-phase framework; that of first taking a set of informative terms from the search results of the initial query, then building a graph, following by using a diversity-conscious node ranking to prioritize candidate terms for diversified query expansion. Our methods differ in the second phase, with the first method Select-Link-Rank (SLR) linking terms with Wikipedia entities to accomplish graph construction; on the other hand, our second method, Select-Embed-Rank (SER), constructs the graph using similarities between distributional word embeddings. Through an empirical analysis and user study, we show that SLR ourperforms state-of-the-art diversified query expansion methods, thus establishing that Wikipedia is an effective resource to aid diversified query expansion. Our empirical analysis also illustrates that SER outperforms the baselines convincingly, asserting that it is the best available method for those cases where SLR is not applicable; these include narrow-focus search systems where a relevant knowledge base is unavailable. Our SLR method is also seen to outperform a state-of-the-art method in the task of diversified entity ranking.

Ranks Aggregation and Semantic Genetic Approach based Hybrid Model for Query Expansion

Effective query expansion terms selection methods are really very important for improving the accuracy and efficiency of Pseudo-Relevance Feedback (PRF) based automatic query expansion techniques in information retrieval system. These methods remove irrelevant and redundant terms from the top retrieved feedback documents with respect to a user query. Individual terms selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual expansion terms selection method that would outperform other individual methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual terms selection methods. Second, we propose a model for combining multiple expansion terms selection methods by using a variety of ranks combining approaches. Third, semantic filtering used to filter out semantically irrelevant terms obtained after combining multiple terms selection methods. Fourth, the Genetic Algorithm used to make an optimal combination of query terms and candidate expansion terms obtained by applying ranks combination and semantic filtering approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over each individual terms selection methods and related state-of-the-arts 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.

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

ABSTRACTQuery 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 other's related state-of-the-art method.