Morphological query expansion and language-filtering words for improving Basque web retrieval

Leturia, Igor; Gurrutxaga, Antton; Areta, Nerea; Alegria, Iñaki; Ezeiza, Aitzol

doi:10.1007/s10579-012-9208-x

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…It combined different weights of each term through using fuzzy rules to infer the weights of the expansion terms. Leturia et al [22] sought to enhance the Basque Web retrieval through QE technique. The authors used morphological query expansion and language filtering words in combination with the APIs of search engines to choose the most appropriate expansion keywords and build the suitable Basque Web search queries.…”

Section: Related Work On Qementioning

confidence: 99%

“…For instance, for standard information retrieval systems, only one step is needed: (i) retrieve the relevant documents to the initial query; whereas, for QE, three steps are required: (i) retrieve the pseudo-relevant documents to the initial query, (ii) extract the best expansion keywords from the pseudorelevant documents to constitute the expanded query, and finally (iii) retrieve the relevant documents to the expanded query. Query expansion is currently used in various applications such as multimedia information retrieval [24], question answering [31], information filtering [22]; and applied to several domains such as sport and game [1], medical and health [9,30] and e-commerce [38].…”

Section: Introductionmentioning

confidence: 99%

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An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Khennak

Drias

2017

Appl Intell

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Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

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Section: Related Work On Qementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Khennak

Drias

2017

Appl Intell

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“…Besides, several computational methods have been appended to the conventional query expansion, which includes (a) generating pseudo-relevant documents, (b) establishing the expanded query by deriving the most relevant codeword from the generated pseudo-relevant documents, and (c) using the expanded query to retrieve the most precise documents. Due to the simplicity of the query expansion technique, it has been used in many applications such as e-commerce, 4 information filtering, 5 sports and games, 6 and medical. 7 A metaheuristic is a high-level problem-oriented structure 8,9 used to evolve the heuristic optimization algorithm by considering the set of procedures provided by it.…”

Section: Introductionmentioning

confidence: 99%

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

Srivel¹,

Kalaiselvi

Shanthi

et al. 2022

Concurrency and Computation

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Due to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy-based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean-Average Precision, F-measure, Precision-recall, and Precision rank are evaluated and analyzed by using TREC-COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state-of-art works. For a total of 60 queries, the proposed model offers an F1-Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1-score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

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A Firefly Algorithm-based Approach for Pseudo-Relevance Feedback: Application to Medical Database

Khennak

Drias

2016

J Med Syst

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The difficulty of disambiguating the sense of the incomplete and imprecise keywords that are extensively used in the search queries has caused the failure of search systems to retrieve the desired information. One of the most powerful and promising method to overcome this shortcoming and improve the performance of search engines is Query Expansion, whereby the user's original query is augmented by new keywords that best characterize the user's information needs and produce more useful query. In this paper, a new Firefly Algorithm-based approach is proposed to enhance the retrieval effectiveness of query expansion while maintaining low computational complexity. In contrast to the existing literature, the proposed approach uses a Firefly Algorithm to find the best expanded query among a set of expanded query candidates. Moreover, this new approach allows the determination of the length of the expanded query empirically. Experimental results on MEDLINE, the on-line medical information database, show that our proposed approach is more effective and efficient compared to the state-of-the-art.

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Morphological query expansion and language-filtering words for improving Basque web retrieval

Cited by 7 publications

References 19 publications

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

A Firefly Algorithm-based Approach for Pseudo-Relevance Feedback: Application to Medical Database

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