A Survey on Important Aspects of Information Retrieval

Information retrieval has become an important field of study and research under computer science due to the explosive growth of information available in the form of full text, hypertext, administrative text, directory, numeric or bibliographic text. The research work is going on various aspects of information retrieval systems so as to improve its efficiency and reliability. This paper presents a comprehensive survey discussing not only the emergence and evolution of information retrieval but also include different information retrieval models and some important aspects such as document representation, similarity measure and query expansion

Consolidated study on query expansion

A typical day of million web users all over the world starts with a simple query. The quest for information on a particular topic drives them to search for it, and in the pursuit of their info the terms they supply for queries varies from person to person depending on the knowledge they have. With a vast collection of documents available on the web universe it is the onus of the retrieval system to return only those documents that are relevant and satisfy the user’s search requirements. The document mismatch problem is resolved by appending extra query terms to the original query which improves the retrieval performance. The addition of terms tends to minimize the bridging-gap between the documents and queries. In this thesis, a brief study is done on the reformulation of queries, along with methods of calculating the relevancy of candidate terms for query expansion by using several ranking algorithms, term weighting algorithms and feedback processes involving evaluations. Comparisons of various methods based on their efficiencies are also discussed. On the whole a consolidated report of query expansion in general is given

Inter-relaão das técnicas Term Extration e Query Expansion aplicadas na recuperação de documentos textuais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Engenharia e Gestão do ConhecimentoConforme Sighal (2006) as pessoas reconhecem a importância do armazenamento e busca da informação e, com o advento dos computadores, tornou-se possível o armazenamento de grandes quantidades dela em bases de dados. Em conseqüência, catalogar a informação destas bases tornou-se imprescindível. Nesse contexto, o campo da Recuperação da Informação, surgiu na década de 50, com a finalidade de promover a construção de ferramentas computacionais que permitissem aos usuários utilizar de maneira mais eficiente essas bases de dados. O principal objetivo da presente pesquisa é desenvolver um Modelo Computacional que possibilite a recuperação de documentos textuais ordenados pela similaridade semântica, baseado na intersecção das técnicas de Term Extration e Query Expansion

Efficient query expansion

Hundreds of millions of users each day search the web and other repositories to meet their information needs. However, queries can fail to find documents due to a mismatch in terminology. Query expansion seeks to address this problem by automatically adding terms from highly ranked documents to the query. While query expansion has been shown to be effective at improving query performance, the gain in effectiveness comes at a cost: expansion is slow and resource-intensive. Current techniques for query expansion use fixed values for key parameters, determined by tuning on test collections. We show that these parameters may not be generally applicable, and, more significantly, that the assumption that the same parameter settings can be used for all queries is invalid. Using detailed experiments, we demonstrate that new methods for choosing parameters must be found. In conventional approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We demonstrate a new method of obtaining expansion terms, based on past user queries that are associated with documents in the collection. The most effective query expansion methods rely on costly retrieval and processing of feedback documents. We explore alternative methods for reducing query-evaluation costs, and propose a new method based on keeping a brief summary of each document in memory. This method allows query expansion to proceed three times faster than previously, while approximating the effectiveness of standard expansion. We investigate the use of document expansion, in which documents are augmented with related terms extracted from the corpus during indexing, as an alternative to query expansion. The overheads at query time are small. We propose and explore a range of corpus-based document expansion techniques and compare them to corpus-based query expansion on TREC data. These experiments show that document expansion delivers at best limited benefits, while query expansion, including standard techniques and efficient approaches described in recent work, usually delivers good gains. We conclude that document expansion is unpromising, but it is likely that the efficiency of query expansion can be further improved

Efficient query expansion with auxiliary data structures

Query expansion is a well-known method for improving average effectiveness in information retrieval. The most effective query expansion methods rely on retrieving documents which are used as a source of expansion terms. Retrieving those documents is costly. We examine the bottlenecks of a conventional approach and investigate alternative methods aimed at reducing query evaluation time. We propose a new method that draws candidate terms from brief document summaries that are held in memory for each document. While approximately maintaining the effectiveness of the conventional approach, this method significantly reduces the time required for query expansion by a factor of 5-10