A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain

Within the space of question answering (QA) systems, the most critical module to improve overall performance is question analysis processing. Extracting the lexical semantic of a Natural Language (NL) question presents challenges at syntactic and semantic levels for most QA systems. This is due to t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ammar Arbaaeen, Asadullah Shah
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	question answering systems knowledge-based method natural language processing word sense disambiguation ontology WordNet Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/3312bf6dadc04772807a65f96c741047
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3312bf6dadc04772807a65f96c741047
record_format	dspace
spelling	oai:doaj.org-article:3312bf6dadc04772807a65f96c7410472021-11-25T17:58:29ZA Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain10.3390/info121104522078-2489https://doaj.org/article/3312bf6dadc04772807a65f96c7410472021-10-01T00:00:00Zhttps://www.mdpi.com/2078-2489/12/11/452https://doaj.org/toc/2078-2489Within the space of question answering (QA) systems, the most critical module to improve overall performance is question analysis processing. Extracting the lexical semantic of a Natural Language (NL) question presents challenges at syntactic and semantic levels for most QA systems. This is due to the difference between the words posed by a user and the terms presently stored in the knowledge bases. Many studies have achieved encouraging results in lexical semantic resolution on the topic of word sense disambiguation (WSD), and several other works consider these challenges in the context of QA applications. Additionally, few scholars have examined the role of WSD in returning potential answers corresponding to particular questions. However, natural language processing (NLP) is still facing several challenges to determine the precise meaning of various ambiguities. Therefore, the motivation of this work is to propose a novel knowledge-based sense disambiguation (KSD) method for resolving the problem of lexical ambiguity associated with questions posed in QA systems. The major contribution is the proposed innovative method, which incorporates multiple knowledge sources. This includes the question’s metadata (date/GPS), context knowledge, and domain ontology into a shallow NLP. The proposed KSD method is developed into a unique tool for a mobile QA application that aims to determine the intended meaning of questions expressed by pilgrims. The experimental results reveal that our method obtained comparable and better accuracy performance than the baselines in the context of the pilgrimage domain.Ammar ArbaaeenAsadullah ShahMDPI AGarticlequestion answering systemsknowledge-based methodnatural language processingword sense disambiguationontologyWordNetInformation technologyT58.5-58.64ENInformation, Vol 12, Iss 452, p 452 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	question answering systems knowledge-based method natural language processing word sense disambiguation ontology WordNet Information technology T58.5-58.64
spellingShingle	question answering systems knowledge-based method natural language processing word sense disambiguation ontology WordNet Information technology T58.5-58.64 Ammar Arbaaeen Asadullah Shah A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain
description	Within the space of question answering (QA) systems, the most critical module to improve overall performance is question analysis processing. Extracting the lexical semantic of a Natural Language (NL) question presents challenges at syntactic and semantic levels for most QA systems. This is due to the difference between the words posed by a user and the terms presently stored in the knowledge bases. Many studies have achieved encouraging results in lexical semantic resolution on the topic of word sense disambiguation (WSD), and several other works consider these challenges in the context of QA applications. Additionally, few scholars have examined the role of WSD in returning potential answers corresponding to particular questions. However, natural language processing (NLP) is still facing several challenges to determine the precise meaning of various ambiguities. Therefore, the motivation of this work is to propose a novel knowledge-based sense disambiguation (KSD) method for resolving the problem of lexical ambiguity associated with questions posed in QA systems. The major contribution is the proposed innovative method, which incorporates multiple knowledge sources. This includes the question’s metadata (date/GPS), context knowledge, and domain ontology into a shallow NLP. The proposed KSD method is developed into a unique tool for a mobile QA application that aims to determine the intended meaning of questions expressed by pilgrims. The experimental results reveal that our method obtained comparable and better accuracy performance than the baselines in the context of the pilgrimage domain.
format	article
author	Ammar Arbaaeen Asadullah Shah
author_facet	Ammar Arbaaeen Asadullah Shah
author_sort	Ammar Arbaaeen
title	A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain
title_short	A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain
title_full	A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain
title_fullStr	A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain
title_full_unstemmed	A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain
title_sort	knowledge-based sense disambiguation method to semantically enhanced nl question for restricted domain
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3312bf6dadc04772807a65f96c741047
work_keys_str_mv	AT ammararbaaeen aknowledgebasedsensedisambiguationmethodtosemanticallyenhancednlquestionforrestricteddomain AT asadullahshah aknowledgebasedsensedisambiguationmethodtosemanticallyenhancednlquestionforrestricteddomain AT ammararbaaeen knowledgebasedsensedisambiguationmethodtosemanticallyenhancednlquestionforrestricteddomain AT asadullahshah knowledgebasedsensedisambiguationmethodtosemanticallyenhancednlquestionforrestricteddomain
_version_	1718411733951840256

A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain

Ejemplares similares