DOCSLIB.ORG

Query Expansion - Automatic Generation of Semantic Similar Phrases Using Wordnet

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Query Expansion - Automatic Generation of Semantic Similar Phrases Using Wordnet “ALEXANDRU IOAN CUZA” UNIVERSITY OF IASI DEPARTMENT OF COMPUTER SCIENCE BACHELOR’S THESIS Query expansion - automatic generation of semantic similar phrases using WordNet Proposed by: Diana Lucaci July 2018 Advisor: Prof. Dr. Adrian Iftene “ALEXANDRU IOAN CUZA” UNIVERSITY OF IASI DEPARTMENT OF COMPUTER SCIENCE Query expansion - automatic generation of semantic similar phrases using WordNet Diana Lucaci July 2018 Advisor: Prof. Dr. Adrian Iftene 1 Avizat, Îndrumător Lucrare de Licență, Conf. dr. Iftene Adrian Data 25.06.2018 Semnătura DECLARAȚIE privind originalitatea conținutului lucrării de licență Subsemnata LUCACI DIANA, cu domiciliul în Gura Humorului, născută la data de 22.02.1996, identificat prin CNP 2960222336529, absolventă a Universității „Alexandru Ioan Cuza” din Iași, Facultatea de Informatică, specializarea Informatică în limba engleză, promoția 2015-2018, declar pe propria răspundere, cunoscând consecințele falsului în declarații în sensul art. 326 din Noul Cod Penal și dispozițiile Legii Educației Naționale nr. 1/2011 art.143 al. 4 și 5 referitoare la plagiat, că lucrarea de licență cu titlul: Query expansion ​ - automatic generation of semantic similar phrases using WordNet, elaborată sub ​ îndrumarea dl. Conf. dr. Iftene Adrian, pe care urmează să o susțin în fața comisiei este originală, îmi aparține și îmi asum conținutul său în întregime. De asemenea, declar că sunt de acord ca lucrarea mea de licență să fie verificată prin orice modalitate legală pentru confirmarea originalității, consimțind inclusiv la introducerea conținutului său într-o bază de date în acest scop. Am luat la cunoștință despre faptul că este interzisă comercializarea de lucrări științifice în vederea facilitării falsificării de către cumpărător a calității de autor al unei lucrări de licență, de diploma sau de disertație și în acest sens, declar pe proprie răspundere că lucrarea de față nu a fost copiată, ci reprezintă rodul cercetării pe care am întreprins-o. Dată azi, Semnătură student 25.06.2018 2 DECLARAȚIE DE CONSIMȚĂMÂNT Prin prezenta declar că sunt de acord ca Lucrarea de licență cu titlul Query ​ expansion - automatic generation of semantic similar phrases using WordNet, ​ codul sursă al programelor și celelalte conținuturi (grafice, multimedia, date de test etc.) care însoțesc această lucrare să fie utilizate în cadrul Facultății de Informatică. De asemenea, sunt de acord ca Facultatea de Informatică de la Universitatea „Alexandru Ioan Cuza” din Iași, să utilizeze, modifice, reproducă și să distribuie în scopuri necomerciale programele-calculator, format executabil și sursă, realizate de mine în cadrul prezentei lucrări de licență. Iași, 25.06.2018 Absolvent Diana Lucaci ____________________ 3 Table of contents Table of contents 4 Abstract 5 Contributions 8 State of the art 9 Synonyms 9 Semantic similarity using WordNet 9 Tweets similarity using WordNet - case study 12 Automatic correction systems 13 Automatic spelling correction using a trigram similarity measure 13 Conceptual distance and automatic spelling correction 14 Corrections systems - conclusions 15 Information retrieval systems 16 Query expansion for information retrieval 17 Stemming 17 Lemmatization 17 Canonicalization 18 Sources for query expansion terms 18 Scoring results 19 Query expansion - conclusions 20 Word embedding 21 Word2Vec 21 GloVe: Global Vectors for Word Representation 22 Proposed solution 23 Architectural model 24 Module 1 - Create a corpus by indexing web articles 25 Module 2 - Generate and filter similar phrases 28 Module 3 - Word embedding. Training set. Neural Network 33 Impact 36 Conclusions 38 Appendix 43 Appendix A - Elasticsearch helper library 43 Appendix B - Examples of Wikipedia revisions 46 Respiratory system 46 Drep 47 4 Abstract Natural Language Processing can be defined as the computational modeling of human language, in computer science relating to formal language theory, compiler techniques, theorem proving, machine learning and human-computer interaction. It is a field of research that covers computer understanding and manipulation of human language, trying to make the machine derive meaning from human language in a smart and useful way, and performing difficult tasks such as information retrieval and extraction, question answering, exam marking, document classification, report generation, automatic summarization and translation, speech recognition, dialogs between human and machine, or other tasks currently performed by humans such as help-desk jobs. The NLP applications are one of the most challenging and popular because of the impact they have on the end user. Replacing help-desks with artificial intelligence, spell checking, automatic translation and virtual assistants are some of the best-known usages of this domain. The progress of the domain regarding synonymity has advanced during the last years, but it lacks accuracy especially for phrases with more than two words. This particular task can have a big impact upon information retrieval systems (a specific application of these type of systems would be a search engine for the medical domain, which is known for its large amount of information that is available and that should be considered before making a decision regarding a diagnosis). Moreover, correction systems could take advantage of similar phrases (not necessarily synonyms), providing alternatives for scientific or grammatical mistakes. As the language is evolving rapidly in this age and new words are being introduced in the vocabulary, this project aims to propose a new strategy of automatically generating similar phrases using the relationships between the concepts from a large lexical database organized as a large graph. The application is structured using independent modules which use the results of previous modules, similar to a waterfall model. In addition, a critical analysis is performed on the results of different approaches, by combining different strategies for each module. If other Query Expansion approaches that use WordNet, such as Improving Query ​ Expansion Using WordNet (Pal et. al., 2013), try to filter a list of possible candidates (e.g. ​ 5 extracted using top-ranked documents) based on the similarity obtained from WordNet, this method extracts new candidates from WordNet that can be missed by using the existing methods and then it filters the result list based on the frequency on a corpus (checking for the validity of a phrase) and also, based on the relevance feedback from an interface (future work). Moreover, the generated phrases serve as a training set for a machine learning model which will perform this task much faster, improving its accuracy over time. The applications of finding the similar phrases can be identified in different systems such as information retrieval applications (search engines), correction and suggestion systems or software that uses large amounts of data. An example from the medical domain would be an application storing the medical records of the patients in order to help the medical staff narrow the search to more specific diagnosis and treatments. Similar treatments could lead to improvements in the treatment that the specialist is considering when dealing with a case. By focusing the result list of the search system on both the exact match and a wider circle of concepts (analysis, medication, prescriptions, etc), it would increase the chances that a doctor finds a new treatment or a similar drug that can be used for the case he or she is dealing with. The thesis consists of a general part that presents the latest approaches of the NLP tasks that are related to the proposed idea and its applications, introducing the most important concepts that are further used to explain the solution and a part that focuses on the technical details, the results and the conclusions of the implementation. State of the art This part introduces the task of generating similar words and phrases, presenting the existing available methods and their applications: correction systems and information retrieval systems, summarizing the steps that are done for the query expansion task and that need to be done before generating similar phrases. In order to better understand the machine learning approach presented in the following chapter, a brief introduction to word embeddings is added as a subchapter of this part. Proposed solution The second part of the thesis consists of the implementation details, the encountered difficulties of the proposed approaches, the results and also the conclusions of each module of the application. Graphics of different types of metrics and a result table is added for a better evaluation of the system. 6 Impact As this system is a proof of concept of the proposed idea, this chapter emphasizes the impact of the application on different areas of study and its proneness of adapting to different use cases. Conclusions The interpretation of the results led to a number of improvements that can be done so that the user to benefit more from the purpose of this application. This chapter emphasizes the possibilities to extend this project, suggesting a few directions for further research. 7 Contributions The system provides an approach that has a wide range of applications in many different domains such as medicine, science, and technology, geography, geology, biology, physics, chemistry. One example would be enhancing existing corpora with new phrases, which is useful to very specific branches of science, where only small corpora exist. This is due to the fact that these
Load more

Recommended publications
  • A Query Suggestion Method Combining TF-IDF and Jaccard Coefficient for Interactive Web Search
    www.sciedu.ca/air Artificial Intelligence Research 2015, Vol. 4, No. 2 ORIGINAL RESEARCH A query suggestion method combining TF-IDF and Jaccard Coefficient for interactive web search Suthira Plansangket ,∗ John Q Gan School of Computer Science and Electronic Engineering, University of Essex, United Kingdom Received: May 10, 2015 Accepted: July 27, 2015 Online Published: August 6, 2015 DOI: 10.5430/air.v4n2p119 URL: http://dx.doi.org/10.5430/air.v4n2p119 Abstract This paper proposes a query suggestion method combining two ranked retrieval methods: TF-IDF and Jaccard coefficient. Four performance criteria plus user evaluation have been adopted to evaluate this combined method in terms of ranking and relevance from different perspectives. Two experiments have been conducted using carefully designed eighty test queries which are related to eight topics. One experiment aims to evaluate the quality of the query suggestions generated by the proposed method, and the other aims to evaluate the improvement of the relevance of retuned documents in interactive web search by using the query suggestions so as to evaluate the effectiveness of the developed method. The experimental results show that the method developed in this paper is the best method for query suggestion among the methods evaluated, significantly outperforming the most popularly used TF-IDF method. In addition, the query suggestions generated by the proposed method significantly improve the relevance of returned documents in interactive web search in terms of increasing the precision or the number of highly relevant documents. Key Words: Query suggestion, Query expansion, Information retrieval, Search engine, Performance evaluation 1 Introduction a cold-start problem.
    [Show full text]
  • Concept-Based Interactive Query Expansion ∗
    Concept-Based Interactive Query Expansion ∗ Bruno M. Fonseca12 Paulo Golgher2 Bruno Pôssas12 [email protected] [email protected] [email protected] Berthier Ribeiro-Neto1 2 Nivio Ziviani1 [email protected] [email protected] ABSTRACT back, web searching. Despite the recent advances in search quality, the fast increase in the size of the Web collection has introduced new challenges for 1. INTRODUCTION Web ranking algorithms. In fact, there are still many situations in The Web is an innovation that has modified the way we learn, which the users are presented with imprecise or very poor results. work and live. The novelty lies not only on the freedom to publish, One of the key difficulties is the fact that users usually submit very but also in the almost universal communication facilities. It marks short and ambiguous queries, and they do not fully specify their in- formation needs. That is, it is necessary to improve the query for- the beginning of a new era, of a new society, started by what we mation process if better answers are to be provided. In this work we may call the information revolution. In these new times, the volume of information that can be ac- propose a novel concept-based query expansion technique, which cessed at low cost and high convenience is mind boggling. To illus- allows disambiguating queries submitted to search engines. The trate, Google1 advertised indexing more than 8 billion Web pages concepts are extracted by analyzing and locating cycles in a special type of query relations graph. This is a directed graph built from in 2004.
    [Show full text]
  • Xu: an Automated Query Expansion and Optimization Tool
    Xu: An Automated Query Expansion and Optimization Tool Morgan Gallant Haruna Isah Farhana Zulkernine Shahzad Khan School of Computing School of Computing School of Computing Gnowit Inc. Queen’s University Queen’s University Queen’s University Ottawa, ON, Canada Kingston, ON, Canada Kingston, ON, Canada Kingston, ON, Canada [email protected] [email protected] [email protected] [email protected] Abstract— The exponential growth of information on the A core issue in IR and search applications is the evaluation Internet is a big challenge for information retrieval systems of search results. The emphasis is on users and their towards generating relevant results. Novel approaches are information needs. The users of an IR system such as a search required to reformat or expand user queries to generate a engine, are the ultimate judges of the quality of the results [2]. satisfactory response and increase recall and precision. Query There are many reasons why a search result may not meet user expansion (QE) is a technique to broaden users’ queries by expectations. Sometimes the query expression may be too introducing additional tokens or phrases based on some short to dictate what the user is looking for or may not be well semantic similarity metrics. The tradeoff is the added formulated [5]. In most cases, the user's original query is not computational complexity to find semantically similar words sufficient to retrieve the information that the user is looking and a possible increase in noise in information retrieval. Despite several research efforts on this topic, QE has not yet been for [6].
    [Show full text]
  • Query Expansion with Locally-Trained Word Embeddings
    Query Expansion with Locally-Trained Word Embeddings Fernando Diaz Bhaskar Mitra Nick Craswell Microsoft Microsoft Microsoft [email protected] [email protected] [email protected] Abstract trained using local windows, risks captur- ing only coarse representations of those top- Continuous space word embeddings ics dominant in the corpus. While a par- have received a great deal of atten- ticular embedding may be appropriate for a tion in the natural language processing specific word within a sentence-length con- and machine learning communities for text globally, it may be entirely inappropri- their ability to model term similarity ate within a specific topic. Gale et al. re- and other relationships. We study the fer to this as the ‘one sense per discourse’ use of term relatedness in the context property (Gale et al., 1992). Previous work of query expansion for ad hoc informa- by Yarowsky demonstrates that this property tion retrieval. We demonstrate that can be successfully combined with informa- word embeddings such as word2vec and tion from nearby terms for word sense dis- GloVe, when trained globally, under- ambiguation (Yarowsky, 1995). Our work ex- perform corpus and query specific em- tends this approach to word2vec-style training beddings for retrieval tasks. These re- in the context word similarity. sults suggest that other tasks benefit- For many tasks that require topic-specific ing from global embeddings may also linguistic analysis, we argue that topic-specific benefit from local embeddings. representations should outperform global rep- resentations. Indeed, it is difficult to imagine 1 Introduction a natural language processing task that would Continuous space embeddings such as not benefit from an understanding of the local word2vec (Mikolov et al., 2013b) or GloVe topical structure.
    [Show full text]
  • A New Query Expansion Method Based on Query Logs Mining1
    International Journal on Asian Language Processing, 19 (1): 1-12 1 A new query expansion method based on query logs mining 1 Zhu Kunpeng, Wang Xiaolong, Liu Yuanchao School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China Email:{kpzhu, wangxl, ycliu}@insun.hit.edu.cn Abstract: Query expansion has long been suggested as an effective way to improve the performance of information retrieval systems by adding additional relevant terms to the original queries. However, most previous research has been limited in extracting new terms from a subset of relevant documents, but has not exploited the information about user interactions. In this paper, we proposed a method for automatic query expansion based on user interactions recorded in query logs. The central idea is to extract correlations among queries by analyzing the common documents the users selected for them, and the expanded terms only come from the associated queries more than the relevant documents. In particular, we argue that queries should be dealt with in different ways according to their ambiguity degrees, which can be calculated from the log information. We verify this method in a large scale query logs collection and the experimental results show that the method makes good use of the knowledge of user interactions, and it can remarkably improve search performance. Keywords: Query expansion, log mining, information retrieval, search engine, 1. Introduction With the rapid growth of information on the World Wide Web, more and more users need search engine technology to help them exploit such an extremely valuable resource. Although many search engine systems have been successfully deployed, the current search systems are still far from optimal because of using simple keywords to search and rank relevant documents.
    [Show full text]
  • Query Expansion Techniques
    Query Expansion techniques Ashish Kankaria Indian Institute of Technology Bombay, Mumbai [email protected] 1. NEED OF QUERY EXPANSION • Creating a dictionary of expansion terms for each terms, The Information Retrieval system described above works and then looking up in the dictionary for expansion very well if the user is able to convey his information need in form of query. But query is seldom complete. The query 2. EXTERNAL RESOURCE BASED QUERY provided by the user is often unstructured and incomplete. An incomplete query hinders a search engine from satisfy- EXPANSION ing the user’s information need. In practice we need some In these approaches, the query is expanded using some ex- representation which can correctly and more importantly ternal resource like WordNet, lexical dictionaries or the- completely express the user’s information need. saurus.These dictionaries are built manually which contain mappings of the terms to their relevant terms. There tech- Figure 1 explains the need of query expansion. Consider niques involve look up in such resources and adding the re- an input query “Sachin Tendulkar”. As a search engine de- lated terms to query. Following are some of external re- veloper we would expect that user wants documents related source based query expansion techniques. [5] to cricketer Sachin Tendulkar. Consider our corpus has 2 documents. First document is an informative page about 2.1 Thesaurus based expansion Sachin Tendulkar which contains the query terms where as A thesaurus is a data structure that lists words grouped to- second document is an blog on Tendulkar which has various gether according to similarity of meaning (containing syn- adjectives related to Sachin Tendulkar like master blaster or onyms and sometimes antonyms), in contrast to a dictio- God of cricket but does not have query terms.
    [Show full text]
  • Learning for Efficient Supervised Query Expansion Via Two-Stage Feature Selection
    Learning for Efficient Supervised Query Expansion via Two-stage Feature Selection Zhiwei Zhang1,∗ Qifan Wang2, Luo Si13 , Jianfeng Gao4 1Dept of CS, Purdue University, IN, USA 2Google Inc, Mountain View, USA 3Alibaba Group Inc, USA 4Microsoft Research, Redmond, WA, USA {zhan1187,lsi}@purdue.edu, [email protected], [email protected] ABSTRACT predicted to be relevant [26]. It is hoped that these expanded Query expansion (QE) is a well known technique to im- terms can capture user's true intent that is missed in orig- prove retrieval effectiveness, which expands original queries inal query, thus improving the final retrieval effectiveness. with extra terms that are predicted to be relevant. A re- In the past decades, various applications [26, 6] have proved cent trend in the literature is Supervised Query Expansion its value. (SQE), where supervised learning is introduced to better se- Unsupervised QE (UQE) algorithms used to be the main- lect expansion terms. However, an important but neglected stream in the QE literature. Many famous algorithms, such issue for SQE is its efficiency, as applying SQE in retrieval as relevance model (RM) [21] and thesaurus based methods can be much more time-consuming than applying Unsuper- [29], have been widely applied. However, recent studies [5, vised Query Expansion (UQE) algorithms. In this paper, 22] showed that a large portion of expansion terms selected we point out that the cost of SQE mainly comes from term by UQE algorithms are noisy or even harmful, which limits feature extraction, and propose a Two-stage Feature Selec- their performance. Supervised Query Expansion (SQE) is tion framework (TFS) to address this problem.
    [Show full text]
  • CEQE: Contextualized Embeddings for Query Expansion
    CEQE: Contextualized Embeddings for Query Expansion Shahrzad Naseri1, Jeffrey Dalton2, Andrew Yates3, and James Allan1 1 University of Massachusetts Amherst, 2University of Glasgow 3Max Planck Institute for Informatics fshnaseri, [email protected], [email protected] [email protected] Abstract. In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextual- ized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new model, Contextu- alized Embeddings for Query Expansion (CEQE), that utilizes query- focused contextualized embedding vectors. We study the behavior of contextual representations generated for query expansion in ad-hoc doc- ument retrieval. We conduct our experiments on probabilistic retrieval models as well as in combination with neural ranking models. We evalu- ate CEQE on two standard TREC collections: Robust and Deep Learn- ing. We find that CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven prob- abilistic pseudo-relevance feedback (PRF) models. We further find that multiple passes of expansion and reranking result in continued gains in effectiveness with CEQE-based approaches outperforming other ap- proaches. The final model incorporating neural and CEQE-based expan- sion score achieves gains of up to 5% in P@20 and 2% in AP on Robust over the state-of-the-art transformer-based re-ranking model, Birch. 1 Introduction Recently there is a significant shift in text processing from high-dimensional word-based representations to ones based on continuous low-dimensional vectors.
    [Show full text]
  • A Survey of Automatic Query Expansion in Information Retrieval
    1 A Survey of Automatic Query Expansion in Information Retrieval CLAUDIO CARPINETO and GIOVANNI ROMANO, Fondazione Ugo Bordoni The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to over- come this limitation is automatic query expansion (AQE), whereby the user’s original query is augmented by new features with a similar meaning. AQE has a long history in the information retrieval community but it is only in the last years that it has reached a level of scientific and experimental maturity, especially in labo- ratory settings such as TREC. This survey presents a unified view of a large number of recent approaches to AQE that leverage various data sources and employ very different principles and techniques. The following questions are addressed. Why is query expansion so important to improve search effectiveness? What are the main steps involved in the design and implementation of an AQE component? What approaches to AQE are available and how do they compare? Which issues must still be resolved before AQE becomes a standard component of large operational information retrieval systems (e.g., search engines)? Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Query formulation; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing General Terms: Algorithms, Experimentation, Measurement, Performance Additional Key Words and Phrases: Query expansion, query refinement, search, word associations, pseudo-relevance feedback, document ranking ACM Reference Format: Carpineto, C. and Romano, G.
    [Show full text]
  • A Query Suggestion Method Combining TF-IDF and Jaccard Coefficient for Interactive Web Search
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Essex Research Repository www.sciedu.ca/air Artificial Intelligence Research 2015, Vol. 4, No. 2 ORIGINAL RESEARCH A query suggestion method combining TF-IDF and Jaccard Coefficient for interactive web search Suthira Plansangket ,∗ John Q Gan School of Computer Science and Electronic Engineering, University of Essex, United Kingdom Received: May 10, 2015 Accepted: July 27, 2015 Online Published: August 6, 2015 DOI: 10.5430/air.v4n2p119 URL: http://dx.doi.org/10.5430/air.v4n2p119 Abstract This paper proposes a query suggestion method combining two ranked retrieval methods: TF-IDF and Jaccard coefficient. Four performance criteria plus user evaluation have been adopted to evaluate this combined method in terms of ranking and relevance from different perspectives. Two experiments have been conducted using carefully designed eighty test queries which are related to eight topics. One experiment aims to evaluate the quality of the query suggestions generated by the proposed method, and the other aims to evaluate the improvement of the relevance of retuned documents in interactive web search by using the query suggestions so as to evaluate the effectiveness of the developed method. The experimental results show that the method developed in this paper is the best method for query suggestion among the methods evaluated, significantly outperforming the most popularly used TF-IDF method. In addition, the query suggestions generated by the proposed method significantly improve the relevance of returned documents in interactive web search in terms of increasing the precision or the number of highly relevant documents.
    [Show full text]
  • Joining Automatic Query Expansion Based on Thesaurus and Word Sense Disambiguation Using Wordnet
    Int. J. Computer Applications in Technology, Vol. 33, No. 4, 2008 271 Joining automatic query expansion based on thesaurus and word sense disambiguation using WordNet Francisco João Pinto* and Antonio Fariña Martínez Department of Computer Science, University of A Coruña, Campus de Elviña s/n, A Coruña, 15071, Spain E-mail: [email protected] E-mail: [email protected] *Corresponding author Carme Fernández Pérez-Sanjulián Department of Galician-Portuguese, French, and Linguistics, University of A Coruña, Campus da Zapateira s/n, A Coruña, 15071, Spain E-mail: [email protected] Abstract: The selection of the most appropriate sense of an ambiguous word in a certain context is one of the main problems in Information Retrieval (IR). For this task, it is usually necessary to count on a semantic source, that is, linguistic resources like dictionaries, thesaurus, etc. Using a methodology based on simulation under a vector space model, we show that the use of automatic query expansion and disambiguation of the sense of the words permits to improve retrieval effectiveness. As shown in our experiments, query expansion is not able by itself to improve retrieval. However, when it is combined with Word Sense Disambiguation (WSD), that is, when the correct meaning of a word is chosen from among all its possible variations, it leads to effectiveness improvements. Keywords: automatic query expansion; thesaurus; disambiguation; WordNet. Reference to this paper should be made as follows: Pinto, F.J., Martínez, A.F. and Pérez-Sanjulián, C.F. (2008) ‘Joining automatic query expansion based on thesaurus and word sense disambiguation using WordNet’, Int. J.
    [Show full text]
  • Query Expansion Techniques for Information Retrieval: a Survey
    This article has been accepted and published in the journal \Information Processing & Management". Content may change prior to final publication. DOI: https://doi.org/10.1016/j.ipm.2019.05.009, 0020-0255/ c 2019 Elsevier Inc. All rights reserved. Noname manuscript No. (will be inserted by the editor) Query Expansion Techniques for Information Retrieval: a Survey Hiteshwar Kumar Azad · Akshay Deepak Received: date / Accepted: date Abstract With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user's initial query is reformulated by adding additional meaningful terms with similar significance. QE { as part of information retrieval (IR) { has long attracted researchers' attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications { bringing out similarities and differences. Keywords Query expansion · Query reformulation · Information retrieval · Internet search 1 Introduction There is a huge amount of data available on the Internet, and it is growing exponentially. This unconstrained information-growth has not been accompanied by a corresponding technical ad- vancement in the approaches for extracting relevant information [191].
    [Show full text]