Query Expansion Approach Research Articles

Personalized Query Expansion with Contextual Word Embeddings

Personalized Query Expansion, the task of expanding queries with additional terms extracted from the user-related vocabulary, is a well-known solution to improve the retrieval performance of a system w.r.t. short queries. Recent approaches rely on word embeddings to select expansion terms from user-related texts. Although promising results have been delivered with former word embedding techniques, we argue that these methods are not suited for contextual word embeddings, which produce a unique vector representation for each term occurrence. In this article, we propose a Personalized Query Expansion method designed to solve the issues arising from the use of contextual word embeddings with the current Personalized Query Expansion approaches based on word embeddings. Specifically, we employ a clustering-based procedure to identify the terms that better represent the user interests and to improve the diversity of those selected for expansion, achieving improvements of up to 4% w.r.t. the best-performing baseline in terms of MAP@100. Moreover, our approach outperforms previous ones in terms of efficiency, allowing us to achieve sub-millisecond expansion times even in data-rich scenarios. Finally, we introduce a novel metric to evaluate the expansion terms’ diversity and empirically show the unsuitability of previous approaches based on word embeddings when employed along with contextual word embeddings, which cause the selection of semantically overlapping expansion terms.

Modified query expansion through generative adversarial networks for information extraction in e-commerce

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. we train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. with the modified CGAN framework, Various forms of semantic insights gathered from the query-document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models.

YouTube X-Rating Detection with Bahasa-Slang Title Using Query Expansion and Rule Based Approaches

The detection of X-rating content on the Internet is still rarely done in Indonesia and the performance of the existing work to detect X-rating content, especially in video is still low. The largest video portal, YouTube, does not yet have automatic X-rating content detection through its content either. Some X-rating content prevention service providers in Indonesia, such as the Internet Positive and Nawala Project, detect X-rating content using the keyword detection method of a web page and then block the web page with DNS filtering. However, that method does not pay attention to using Bahasa-Slang. This work developed Metasearch named Safedio. Safedio aims to detect X-rating content on YouTube content through video titles that contain Bahasa-Slang. Safedio utilizes Query Expansion and Rule-Based approaches. The Query Expansion is a technique to get additional rules in search. In the end, Safedio can detect X-rating content through video titles in both Bahasa and Bahasa-Slang. The average results return with precision 71%, recall 46% and accuracy 72%.

Improving query expansion using pseudo-relevant web knowledge for information retrieval

• Web knowledge-based query expansion technique uses the top N pseudo relevant web pages • Top N pseudo relevant web pages returned by search engines Google, Bing, and DuckDuckGo as data sources • Proposed weighting techniques have properly capture the term relationship while weighing the expansion terms • Experimental results show an improvement of 25.89% over the unexpanded queries of FIRE dataset In the field of information retrieval, query expansion (QE) has long been used as a technique to deal with the fundamental issue of word mismatch between a user’s query and the target information. In the context of the relationship between the query and expanded terms, existing weighting techniques often fail to appropriately capture the term-term relationship and term to the whole query relationship, resulting in low retrieval effectiveness. Our proposed QE approach addresses this by proposing three weighting models based on (1) tf-idf, (2) k-nearest neighbor (kNN) based cosine similarity , and (3) correlation score. Further, to extract the initial set of expanded terms, we use pseudo-relevant web knowledge consisting of the top N web pages returned by the three popular search engines namely, Google, Bing, and DuckDuckGo, in response to the original query. Among the three weighting models, tf-idf scores each of the individual terms obtained from the web content, kNN-based cosine similarity scores the expansion terms to obtain the term-term relationship, and correlation score weighs the selected expansion terms with respect to the whole query. The proposed model, called web knowledge based query expansion (WKQE), achieves an improvement of 25.89% on the Mean Average Precision (MAP) score and 30.83% on the Geometric Mean Average precision (GMAP) score over the unexpanded queries on the FIRE dataset. A comparative analysis of the WKQE techniques with other related approaches clearly shows significant improvement in the retrieval performance. We have also analyzed the effect of varying the number of pseudo-relevant documents and expansion terms on the retrieval effectiveness of the proposed model.

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.

An evolutionary game theory based approach for query expansion

An evolutionary game theory based approach for query expansion

Word-embedding-based query expansion: Incorporating Deep Averaging Networks in Arabic document retrieval

One of the main issues associated with search engines is the query–document vocabulary mismatch problem, a long-standing problem in Information Retrieval (IR). This problem occurs when a user query does not match the content of stored documents, and it affects most search tasks. Automatic query expansion (AQE) is one of the most common approaches used to address this problem. Various AQE techniques have been proposed; these mainly involve finding synonyms or related words for the query terms. Word embedding (WE) is one of the methods that are currently receiving significant attention. Most of the existing AQE techniques focus on expanding the individual query terms rather the entire query during the expansion process, and this can lead to query drift if poor expansion terms are selected. In this article, we introduce Deep Averaging Networks (DANs), an architecture that feeds the average of the WE vectors produced by the Word2Vec toolkit for the terms in a query through several linear neural network layers. This average vector is assumed to represent the meaning of the query as a whole and can be used to find expansion terms that are relevant to the complete query. We explore the potential of DANs for AQE in Arabic document retrieval. We experiment with using DANs for AQE in the classic probabilistic BM25 model as well as for two recent expansion strategies: Embedding-Based Query Expansion approach (EQE1) and Prospect-Guided Query Expansion Strategy (V2Q). Although DANs did not improve all outcomes when used in the BM25 model, it outperformed all baselines when incorporated into the EQE1 and V2Q expansion strategies.

Chatbot4QR: Interactive Query Refinement for Technical Question Retrieval

Technical Q&#x0026;A sites (e.g., Stack Overflow (SO)) are important resources for developers to search for knowledge about technical problems. Search engines provided in Q&#x0026;A sites and information retrieval approaches (e.g., word embedding-based) have limited capabilities to retrieve relevant questions when queries are imprecisely specified, such as missing important technical details (e.g., the user&#x2019;s preferred programming languages). Although many automatic query expansion approaches have been proposed to improve the quality of queries by expanding queries with relevant terms, the information missed in a query is not identified. Moreover, without user involvement, the existing query expansion approaches may introduce unexpected terms and lead to undesired results. In this paper, we propose an interactive query refinement approach for question retrieval, named <i>Chatbot4QR</i>, which can assist users in recognizing and clarifying technical details missed in queries and thus retrieve more relevant questions for users. Chatbot4QR automatically detects missing technical details in a query and generates several clarification questions (CQs) to interact with the user to capture their overlooked technical details. To ensure the accuracy of CQs, we design a heuristic-based approach for CQ generation after building two kinds of technical knowledge bases: a manually categorized result of 1,841 technical tags in SO and the multiple version-frequency information of the tags. We develop a Chatbot4QR prototype that uses 1.88 million SO questions as the repository for question retrieval. To evaluate Chatbot4QR, we conduct six user studies with 25 participants on 50 experimental queries. The results are as follows. (1) On average 60.8 percent of the CQs generated for a query are useful for helping the participants recognize missing technical details. (2) Chatbot4QR can rapidly respond to the participants after receiving a query within approximately 1.3 seconds. (3) The refined queries contribute to retrieving more relevant SO questions than nine baseline approaches. For more than 70 percent of the participants who have preferred techniques on the query tasks, Chatbot4QR significantly outperforms the state-of-the-art word embedding-based retrieval approach with an improvement of at least 54.6 percent in terms of two measurements: Pre<inline-formula><tex-math notation="LaTeX">$@$</tex-math><alternatives><mml:math><mml:mo>@</mml:mo></mml:math><inline-graphic xlink:href="xia-ieq1-3016006.gif"/></alternatives></inline-formula>k and NDCG<inline-formula><tex-math notation="LaTeX">$@$</tex-math><alternatives><mml:math><mml:mo>@</mml:mo></mml:math><inline-graphic xlink:href="xia-ieq2-3016006.gif"/></alternatives></inline-formula>k. (4) For 48-88 percent of the assigned query tasks, the participants obtain more desired results after interacting with Chatbot4QR than directly searching from Web search engines (e.g., the SO search engine and Google) using the original queries.

A discriminative method for global query expansion and term reweighting using co-occurrence graphs

This article presents a new query expansion (QE) method aiming to tackle term mismatch in information retrieval (IR). Previous research showed that selecting good expansion terms which do not hurt retrieval effectiveness remains an open and challenging research question. Our method investigates how global statistics of term co-occurrence can be used effectively to enhance expansion term selection and reweighting. Indeed, we build a co-occurrence graph using a context window approach over the entire collection, thus adopting a global QE approach. Then, we employ a semantic similarity measure inspired by the Okapi BM25 model, which allows to evaluate the discriminative power of words and to select relevant expansion terms based on their similarity to the query as a whole. The proposed method includes a reweighting step where selected terms are assigned weights according to their relevance to the query. What’s more, our method does not require matrix factorisation or complex text mining processes. It only requires simple co-occurrence statistics about terms, which reduces complexity and insures scalability. Finally, it has two free parameters that may be tuned to adapt the model to the context of a given collection and control co-occurrence normalisation. Extensive experiments on four standard datasets of English (TREC Robust04 and Washington Post) and French (CLEF2000 and CLEF2003) show that our method improves both retrieval effectiveness and robustness in terms of various evaluation metrics and outperforms competitive state-of-the-art baselines with significantly better results. We also investigate the impact of varying the number of expansion terms on retrieval results.

Semantic approaches for query expansion

To increase the Information Retrieval System’s efficiency, there is a requirement to expand the native user query. There are many approaches to enhance the user query in which the primary method consists of semantic-based query expansion (QE). In a semantic-based QE approach, relevant documents are retrieved by considering all the similar terms of a given user query. The semantic-based QE approach helps in dealing with the limitation of low recall and low precision value of the Information retrieval system and deals with ambiguity and vagueness. Query Expansion techniques contain the semantic concepts that are relevant to semantic computing, computational intelligence, and information retrieval area. Computational intelligence technique is required during automatic query expansion for advanced information processing. This paper presents a comprehensive survey of the methods proposed by several researchers considering semantic-based query expansion. It also discusses the merits and demerits of each technique in a detailed manner. This paper presents the view of several semantic query expansion core techniques. This paper also discusses the various keyholes present in today’s era.

Query expansion based on term distribution and DBpedia features

Query Expansion (QE) approaches that involve the reformulation of queries by adding new terms to the initial user query, are intended to ameliorate the vocabulary mismatch between the query keywords and the documents’ in Information Retrieval Systems (IRS). One big issue in QE is the selection of the right candidate terms for expansion. For this purpose Linked Data can be used, as a valuable resource, for providing additional expansion features such as the values of sub- and super classes of resources. The underlying research question is whether interlinked data and vocabulary items provide features which can be taken into account for query expansion. In this paper, we introduced a new QE approach that aimed at improving IRS by using the well-known distribution based method Bose-Einstein statistics (Bo1) as well as Linked Data from the knowledge base DBpedia using different numbers of expansion terms. We evaluated the effectiveness of each method individually as well as their combinations using two Text REtrieval Conference (TREC) test collections. Our approach has lead to significant improvement in terms of precision, recall, Mean Average Precision (MAP) at rank 10, and normalized Discounted Cumulative Gain (nDCG) at different ranks compared to Pseudo Relevance Feedback (PRF) that we used as a baseline. The results show that the inclusion of semantic annotations clearly improves the retrieval performance over the baseline 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.

Query specific graph-based query reformulation using UMLS for clinical information access.

Biomedical document retrieval requires entity level processing instead of term level. This paper explores the usage and impact of UMLS for entity-based query reformulation in biomedical document retrieval. A novel graph-based approach for query reformulation using UMLS is described herein which queries are expanded using biomedical entities. The proposed method considers UMLS entities from a query with their related entities identified by UMLS and constructs a query-specific graph of biomedical entities for term selection. This query reformulation approach is compared with baseline, pseudo relevance feedback based query expansion approach and state-of-the-art UMLS based query reformulation approaches. The experiments on CDS 2015 and CDS 2016 datasets shows 35% and 45% improvement in retrieval performance, respectively.

Channel retrieval: finding relevant broadcasters on Telegram

Mobile broadband (3G and 4G) has remarkably influenced people’s lives. For instance, the impacts of this technology are evident in transportation, education and messaging. With the advent of this technology, a new generation of messengers offering instant messaging is available to people. One of them is Telegram that comes with new features; one of them is broadcasting messages under the name of “channel.” In this paper, we introduce the channel retrieval problem which aims to find a sorted list of related channels to a user query. This problem is first modeled to the classic information retrieval problems (expert finding and blog retrieval), but since there’s a vocabulary gap between the user query and the published messages in the channels, two query expansion methods for enhancing the performance are proposed. In this paper, a dataset is generated for the channel retrieval, which is publicly available for other researchers. Our experiments on this dataset show that using a semantic approach for query expansion can enhance channel retrieval performance.

Feature Weighting in Finding Feedback Documents for Query Expansion in Biomedical Document Retrieval

Finding good feedback documents for query expansion is a well-known problem in the field of information retrieval. This paper describes a novel approach for finding relevant documents for feedback in query expansion for biomedical document retrieval. The proposed approach relies on a small amount of human intervention to find good feedback documents and tries to learn the relation between query and documents in terms of usefullness of document for query expansion. This proposed approach uses an NLP-based feature weighting technique with classification and clustering method on the documents and identifies relevant documents for feedback. The documents are represented using term frequency and inverse document frequency (TF–IDF) features and these features are weighted according to the type of query and type of the terms. The experiments performed on CDS 2014, 2015 and 2016 datasets show that the feature weighting in finding feedback documents for query expansion approach gives good results as compared to the results of pseudo-relevance feedback, relevance feedback and the results of TF–IDF features without weighting.

QuantQueryEXP : A novel strategic approach for query expansion based on quantum computing principles

In our current world, numerous innovative approaches aimed at expansion and recommendation of queries are developing to satisfy the mounting tendencies of social networking and the internet. Many effectual procedures have been developed to achieve query expansion. Additionally, the creation of quantum-based procedures is now becoming an important task. Quantum based algorithms are the way to secure the future of computation by preparing for the advent of quantum computers. QuantQueryEXP implements an eminent technique, the Deutsch-Jozsa [1] algorithm, in an altered manner, delivering a substitute for expansion and recommendation of queries implementing quantum techniques which offers much higher accuracy with optimization in complexity over any conventional algorithm to achieve supremacy in quantum computing. QuantQueryEXP attains an accuracy of 90.43% along with great optimization of resource and temporal complexity.

A Study on Ranking Fusion Approaches for the Retrieval of Medical Publications

In this work, we compare and analyze a variety of approaches in the task of medical publication retrieval and, in particular, for the Technology Assisted Review (TAR) task. This problem consists in the process of collecting articles that summarize all evidence that has been published regarding a certain medical topic. This task requires long search sessions by experts in the field of medicine. For this reason, semi-automatic approaches are essential for supporting these types of searches when the amount of data exceeds the limits of users. In this paper, we use state-of-the-art models and weighting schemes with different types of preprocessing as well as query expansion (QE) and relevance feedback (RF) approaches in order to study the best combination for this particular task. We also tested word embeddings representation of documents and queries in addition to three different ranking fusion approaches to see if the merged runs perform better than the single models. In order to make our results reproducible, we have used the collection provided by the Conference and Labs Evaluation Forum (CLEF) eHealth tasks. Query expansion and relevance feedback greatly improve the performance while the fusion of different rankings does not perform well in this task. The statistical analysis showed that, in general, the performance of the system does not depend much on the type of text preprocessing but on which weighting scheme is applied.

Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering

Question Answering (QA) systems based on Information Retrieval return precise answers to natural language questions, extracting relevant sentences from document collections. However, questions and sentences cannot be aligned terminologically, generating errors in the sentence retrieval. In order to augment the effectiveness in retrieving relevant sentences from documents, this paper proposes a hybrid Query Expansion (QE) approach, based on lexical resources and word embeddings, for QA systems. In detail, synonyms and hypernyms of relevant terms occurring in the question are first extracted from MultiWordNet and, then, contextualized to the document collection used in the QA system. Finally, the resulting set is ranked and filtered on the basis of wording and sense of the question, by employing a semantic similarity metric built on the top of a Word2Vec model. This latter is locally trained on an extended corpus pertaining the same topic of the documents used in the QA system. This QE approach is implemented into an existing QA system and experimentally evaluated, with respect to different possible configurations and selected baselines, for the Italian language and in the Cultural Heritage domain, assessing its effectiveness in retrieving sentences containing proper answers to questions belonging to four different categories.

Enhance code search via reformulating queries with evolving contexts

To improve code search, many query expansion (QE) approaches use APIs or crowd knowledge for expanding a query. However, these approaches may sometimes negatively impact the retrieval performance. This is because they can’t distinguish the relevant terms from the irrelevant ones among a large set of candidate expansion terms and expand a query with irrelevant terms. In this paper, we propose QREC, a query reformulation approach with evolving contexts that refer to new/deleted terms and dependent terms during the code evolution. By considering the new terms as the relevant and the deleted terms as the irrelevant, QREC could reformulate a query with appropriate expansion terms. The experimental results show that QREC outperforms the state-of-the-art QE approaches (e.g., CodeHow and QECK) by 9–11% and improves the precision of the code search algorithms IR, Portfolio and VF by up to 37–45%.