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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/11/452 |
| _version_ | 1827676455606157312 |
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| author | Ammar Arbaaeen Asadullah Shah |
| author_facet | Ammar Arbaaeen Asadullah Shah |
| author_sort | Ammar Arbaaeen |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:24:28Z |
| format | Article |
| id | doaj.art-3312bf6dadc04772807a65f96c741047 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T05:24:28Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-3312bf6dadc04772807a65f96c7410472023-11-22T23:45:25ZengMDPI AGInformation2078-24892021-10-01121145210.3390/info12110452A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted DomainAmmar Arbaaeen0Asadullah Shah1Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, MalaysiaFaculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, MalaysiaWithin 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.https://www.mdpi.com/2078-2489/12/11/452question answering systemsknowledge-based methodnatural language processingword sense disambiguationontologyWordNet |
| spellingShingle | Ammar Arbaaeen Asadullah Shah A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain Information question answering systems knowledge-based method natural language processing word sense disambiguation ontology WordNet |
| title | 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_short | 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 |
| topic | question answering systems knowledge-based method natural language processing word sense disambiguation ontology WordNet |
| url | https://www.mdpi.com/2078-2489/12/11/452 |
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