Sense-Based Information Retrieval Using Fuzzy Logic and Swarm Intelligence

Hassan, Alia Karim Abdul; Hadi, Mustafa Jasim

doi:10.14257/ijmue.2017.12.1.31

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…These issues include reduction in the quality of the results because of resource selection, employing ambiguous words in the query and high response time resulting from the use of traditional search with the inverted index. A model was developed by [27] that employs the fuzzy logic and artificial bee colony (ABC) intelligence to enhance information retrieval systems" performance. A nearest neighbor graph is employed to modify ABC and improve the searching process.…”

Section: Meta-heuristic For Wsdmentioning

confidence: 99%

Metaheuristic for Word Sense Disambiguation: a Review

Al-Saiagh

Tiun

Al-Saffar

et al. 2018

IJET

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Word Sense Disambiguation (WSD) is the process of determining the exact sense of a particular word in accordance to the context in a computational manner. Such task plays an essential role in multiple fields of study such as Information Retrieval and Information Extraction. With the complexity of human language, WSD came up to solve the problem behind the ambiguity between senses in which a single word would yield different meaning. In this vein, determining the exact meaning of the certain word would facilitate the process of identifying the category of such text, accurate corresponding search results and providing an accurately summarized portion. Several approaches have been proposed for the WSD including statistical, semantic and machine learning techniques. This paper aims to provide a review of such approaches by tackling and categorizing the related works in accordance to the main types.

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Section: Meta-heuristic For Wsdmentioning

confidence: 99%

Metaheuristic for Word Sense Disambiguation: a Review

Al-Saiagh

Tiun

Al-Saffar

et al. 2018

IJET

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show abstract

“…For a human, it is rather simple to recognise the right sense under a certain context, but for a computer, word sense disambiguation (WSD) is a rather difficult task as it is considered as an AI‐complete problem [1]. WSD has become one of the central challenges in natural language rocessing field and is widely used in machine translation [2,3], information retrieval [4], information extraction [5], name disambiguation [6] etc. In machine translation, an optimally machine translation system usually incorporate some kind of WSD component, a WSD system can help a machine translation system to choose the right translation for an ambiguous word.…”

Section: Introductionmentioning

confidence: 99%

TWE‐WSD: An effective topical word embedding based word sense disambiguation

Jia

Tang

et al. 2021

CAAI Trans on Intel Tech

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Word embedding has been widely used in word sense disambiguation (WSD) and many other tasks in recent years for it can well represent the semantics of words. However, the existing word embedding methods mostly represent each word as a single vector, without considering the homonymy and polysemy of the word; thus, their performances are limited. In order to address this problem, an effective topical word embedding (TWE)-based WSD method, named TWE-WSD, is proposed, which integrates Latent Dirichlet Allocation (LDA) and word embedding. Instead of generating a single word vector (WV) for each word, TWE-WSD generates a topical WV for each word under each topic. Effective integrating strategies are designed to obtain high quality contextual vectors. Extensive experiments on SemEval-2013 and SemEval-2015 for English all-words tasks showed that TWE-WSD outperforms other state-of-the-art WSD methods, especially on nouns. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Word sense disambiguation using hybrid swarm intelligence approach

et al. 2018

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Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches.

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Sense-Based Information Retrieval Using Fuzzy Logic and Swarm Intelligence

Abstract: Improvement of the quality of information retrieval (IR)

Cited by 5 publications

References 19 publications

Metaheuristic for Word Sense Disambiguation: a Review

Metaheuristic for Word Sense Disambiguation: a Review

TWE‐WSD: An effective topical word embedding based word sense disambiguation

Word sense disambiguation using hybrid swarm intelligence approach

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