Abstract
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.
Highlights
IntroductionThe emerging field of information retrieval (IR) based on question answering (QA)
The emerging field of information retrieval (IR) based on question answering (QA)systems integrates the research and techniques from natural language processing (NLP), information extraction (IE), automatic summarization, knowledge representation and database systems
knowledge-based sense disambiguation (KSD) aims to determine the intended meaning and assign the correct sense to the polysemous words that appear in Natural Language (NL) questions to enhance the QA system performance
Summary
The emerging field of information retrieval (IR) based on question answering (QA). Systems integrates the research and techniques from natural language processing (NLP), information extraction (IE), automatic summarization, knowledge representation and database systems. In QA applications, the user may obtain precise and concise answers to questions from the stored documents. While in IR applications, the user searches by keywords as input and receives a relevant list of documents based on the query [1]. The research in QA, where a system is required to understand NL questions and infer precise answers, has recently drawn considerable attention. NLP supports QA systems with understanding expressions techniques to comprehend the NL questions. IE utilises NLP techniques and tasks such as name entity recognition, vocabulary analysis, template elements and relationship extraction to generate structured machinereadable format from text to obtain appropriate information.
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