“Query expansion” papers · AI for Research

Query Expansion

Ahlgren, Per · Borås Academic Digital Archive (University of Borås) · 2002

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Methods: Query expansion uses several processes from Natural Language Processing. In most cases when implementing query understanding systems, a simple or sometimes very complex spell checker is used. In this project; however, our first step in this project is normalization. In normalization process we use Hazm library normalizer. The output undergoes a stemming algorithm which is partly from Hazm algorithm used with some modifications. Some suffixes were removed from the algorithm. Then a simple tokenizer is applied to the output so that tokens will be divided. The most important part of the research is the lexicon. I extracted the vocabularies and their synonyms from the book: فرهنگ جامع واژگان مترادف و متضاد زبان فارسي written by: فرجاالله خداپرستي After preprocessing of the texts from the book, and cleaning the data I dumped the words and their synonyms into a python pickle file. Python pickle is a module for serializing and de-serializing python objects. With pickle python objects will be converted into byte streams. Then I wrote a module to load this pickle file and get the input query. It will then process the input query and find the synonyms and show the nearest queries in the sense of meaning.

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Query expansion

2014

edi ret s of doc sam tero uer rfo is se query dependent search methods make cross-media retrieval performance not only accurate but also stable. We also propose different learning methods of relevance feedback (RF) to improve the performance. Experiment is encouraging and validates the proposed methods. rmatio f resea etrieva

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Query expansion

Advisor Stephanie, W. Haas, Vijay Deepak Dollu et al. · 2005

This literature review provides an overview of the various topics related to using implicit feedback for information retrieval. Specific topics which are addressed are explicit relevance feedback for query expansion and its advantages and disadvantages, user behaviors that provide implicit information to a system, techniques to implement implicit feedback in systems and the advantages and disadvantages of using implicit feedback in information retrieval systems. For behaviors of users that were studied by researchers, this study provides a discussion on whether the researchers found them to be useful or not. A discussion on the techniques with which these behaviors are implemented in systems by researchers is presented to gain an understanding of how these behaviors could be used in a system to interpret the behaviors of users. It is concluded that information retrieval systems utilizing implicit feedback would help users find relevant documents. Headings:

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Query Expansion

SpringerReference · 2011

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Query Expansion

Rahmi, Adel · Figshare · 2018

Methods: Query expansion uses several processes from Natural Language Processing. In most cases when implementing query understanding systems, a simple or sometimes very complex spell checker is used. In this project; however, our first step in this project is normalization. In normalization process we use Hazm library normalizer. The output undergoes a stemming algorithm which is partly from Hazm algorithm used with some modifications. Some suffixes were removed from the algorithm. Then a simple tokenizer is applied to the output so that tokens will be divided. The most important part of the research is the lexicon. I extracted the vocabularies and their synonyms from the book: فرهنگ جامع واژگان مترادف و متضاد زبان فارسي written by: فرجاالله خداپرستي After preprocessing of the texts from the book, and cleaning the data I dumped the words and their synonyms into a python pickle file. Python pickle is a module for serializing and de-serializing python objects. With pickle python objects will be converted into byte streams. Then I wrote a module to load this pickle file and get the input query. It will then process the input query and find the synonyms and show the nearest queries in the sense of meaning.

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Query Expansion

Reda Alhajj, Jon Rokne · 2018

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query-expansion

Simeon Emanuilov · Hugging Face · 2024

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Query Expansion

Adel Rahmi · Open MIND · 2018

Methods: Query expansion uses several processes from Natural Language Processing. In most cases when implementing query understanding systems, a simple or sometimes very complex spell checker is used. In this project; however, our first step in this project is normalization. In normalization process we use Hazm library normalizer. The output undergoes a stemming algorithm which is partly from Hazm algorithm used with some modifications. Some suffixes were removed from the algorithm. Then a simple tokenizer is applied to the output so that tokens will be divided. The most important part of the research is the lexicon. I extracted the vocabularies and their synonyms from the book: فرهنگ جامع واژگان مترادف و متضاد زبان فارسي written by: فرجاالله خداپرستي After preprocessing of the texts from the book, and cleaning the data I dumped the words and their synonyms into a python pickle file. Python pickle is a module for serializing and de-serializing python objects. With pickle python objects will be converted into byte streams. Then I wrote a module to load this pickle file and get the input query. It will then process the input query and find the synonyms and show the nearest queries in the sense of meaning.

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Query Expansion

Ahlgren, Per · Borås Academic Digital Archive (University of Borås) · 2002

Cited by 0

Query expansion.

Patricia L. Whetzel (798467), Jeffrey S. Grethe (798468), Davis E. Banks (798469) et al. · Figshare · 2015

The included terms are additional terms used to query the data sources based on the term expansion process using knowledge from the ontology. The most common expansion includes synonyms and common abbreviations of a term.

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Personalized Query Expansion

Afsa Hameed · International Journal of Information Systems and Computer Technologies · 2023

The rapid expansion of data on the web has largely increased the problems associated with efficient searching and retrieval of required data that fulfills the user’s needs. To resolve the ineffectiveness of information retrieval systems query expansion technology is employed. In this paper, the main focus is laid on the expansion of queries by using each user’s profile and search history. In this survey paper different personalized query expansion techniques are compared. This survey's main goal is to present all the ideas, technologies, and applications being employed for personalized query expansion.

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Diversified query expansion

Arbi Bouchoucha · Papyrus : Institutional Repository (Université de Montréal) · 2015

La diversification des résultats de recherche (DRR) vise à sélectionner divers documents à partir des résultats de recherche afin de couvrir autant d’intentions que possible. Dans les approches existantes, on suppose que les résultats initiaux sont suffisamment diversifiés et couvrent bien les aspects de la requête. Or, on observe souvent que les résultats initiaux n’arrivent pas à couvrir certains aspects. Dans cette thèse, nous proposons une nouvelle approche de DRR qui consiste à diversifier l’expansion de requête (DER) afin d’avoir une meilleure couverture des aspects. Les termes d’expansion sont sélectionnés à partir d’une ou de plusieurs ressource(s) suivant le principe de pertinence marginale maximale. Dans notre première contribution, nous proposons une méthode pour DER au niveau des termes où la similarité entre les termes est mesurée superficiellement à l’aide des ressources. Quand plusieurs ressources sont utilisées pour DER, elles ont été uniformément combinées dans la littérature, ce qui permet d’ignorer la contribution individuelle de chaque ressource par rapport à la requête. Dans la seconde contribution de cette thèse, nous proposons une nouvelle méthode de pondération de ressources selon la requête. Notre méthode utilise un ensemble de caractéristiques qui sont intégrées à un modèle de régression linéaire, et génère à partir de chaque ressource un nombre de termes d’expansion proportionnellement au poids de cette ressource. Les méthodes proposées pour DER se concentrent sur l’élimination de la redondance entre les termes d’expansion sans se soucier si les termes sélectionnés couvrent effectivement les différents aspects de la requête. Pour pallier à cet inconvénient, nous introduisons dans la troisième contribution de cette thèse une nouvelle méthode pour DER au niveau des aspects. Notre méthode est entraînée de façon supervisée selon le principe que les termes reliés doivent correspondre au même aspect. Cette méthode permet de sélectionner des termes d’expansion à un niveau sémantique latent afin de couvrir autant que possible différents aspects de la requête. De plus, cette méthode autorise l’intégration de plusieurs ressources afin de suggérer des termes d’expansion, et supporte l’intégration de plusieurs contraintes telles que la contrainte de dispersion. Nous évaluons nos méthodes à l’aide des données de ClueWeb09B et de trois collections de requêtes de TRECWeb track et montrons l’utilité de nos approches par rapport aux méthodes existantes.

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• Semantic query expansion

Doug Tudhope, Ceri Binding · 2010

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Efficient query expansion

Von Billerbeck, Bodo · Figshare · 2011

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.

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Lazy Query Expansion

Alexander Gelbukh · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2011

gelbukh*cic.ipn.mx An information retrieval or document base system has to somehow deal with various phenomena of equivalence of some strings. These are lowercase versus uppercase matching, morphological inflection, derivation, and synonymy of words: e.g., given a query computer, find Computers, computing, workstation. The latter problems are very important in languages with richer morphology and less stable terminology than in English. Also, much better recall is achieved by matching hyponyms and hypernyms using a thesaurus, e.g., given a query computers, find also supercomputer, microcomputer, mainframe, machine, device, processor, UNIX, etc. Technically, this can be handled at the time of indexing by reducing related strings to a common form, or at the time of query processing by expanding the query with the whole set of the related forms. We argue for that the latter way allows for greater flexibility and easier maintenance, while being more affordable than it is usually considered. We propose to expand the query with only those words that really appear in the document base. Our experiments with a thesaurus-based information retrieval system we are developing for the Senate of Mexican Republic show only insignificant increase of the real user queries on average with the 200-megabyte document base of the Senate, in spite of highly inflective Spanish language.

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Collaborative Query Expansion

Maria Chli, Philippe De Wilde · 2009

Cited by 1

Conceptual query expansion

Xue-dong Yang, Yiyu Yao · 2005

Abstract. Query expansion has been extensively studied as a technique for increasing information retrieval performance. However, due to the volume of documents available on the web, many of the techniques that have been successful in traditional information retrieval systems do not scale well to web information retrieval. We propose a new technique based on conceptual semantic theories, in contrast to the structuralist semantic theories upon which other techniques are based. The source of the query expansion information is the concept network knowledge base. Query terms are matched to those contained in the concept network, from which concepts are deduced and additional query terms are selected. In this paper, we describe the theoretical basis for this in-progress research, along with some preliminary results. 1

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Conceptual Query Expansion

F.A. Grootjen, Th.P. van der Weide · Data Archiving and Networked Services (DANS) · 2004

Without detailed knowledge of a collection, most users find it difficult to formulate effective queries. In fact, as observed from Web search engines, users may spend large amounts of time reformulating their queries in order to satisfy their information need. A proven successful method to overcome this difficulty is to treat the query as an initial attempt to retrieve information and use it to construct a new, hopefully better query. Another way to improve a query is to use global (thesauri-like) information. In this article a new, hybrid approach is presented that projects the initial query result on the global information, leading to a local conceptual overview. The resulting concepts are candidates for query refinement.

Cited by 4Open access

Refining Query Expansion Terms using Query Context

Reuben Crimp, Andrew Trotman · 2018

Query expansion is commonly used to combat the vocabulary mismatch problem, it bridges the disparity between the vocabulary used in the corpus and search queries. However, if expansion terms are not chosen carefully, there is a risk of including spurious expansion terms, which can broaden the potential interpretations of the modified query. Unintentionally increasing the semantic ambiguity in this way is known as query drift.

Cited by 6

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