Machine learning based review on Development and Classification of Question-Answering Systems

Sayli Uttarwar, Simran Gambani, Tej Thakkar, Nikahat Mulla
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Abstract

Humans seek information continuously. In order to make information access easier, question answering (henceforth mentioned as QA) systems are developed with which user can interact in natural language and obtain relevant response. From using primitive methodologies to making the system intelligent and self-sufficient, many significant research advancements have been made in this domain since the 1960s. In this paper, we present a survey that aims to summarize the developmental trends in implementation of QA systems over the years. The paper mentions research classified under the identified important characteristics of any QA system. Consequently, an attempt is made to get a concise picture of the most current state of research in this domain. Following the review of this research, we have identified some areas having scope for future development like conversational QA systems, enhancing cognitive abilities of QA systems, etc.
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基于机器学习的问答系统发展与分类综述
人类不断地寻求信息。为了方便信息的获取,人们开发了问答(以下简称QA)系统,用户可以用自然语言进行交互,并获得相应的响应。从使用原始的方法到使系统智能化和自给自足,自20世纪60年代以来,这一领域的研究取得了许多重大进展。在本文中,我们提出了一项调查,旨在总结多年来质量保证系统实施的发展趋势。本文提到了对任何QA系统的重要特征进行分类的研究。因此,本文试图对这一领域的最新研究状况作一个简明的描述。根据这项研究的回顾,我们已经确定了一些有未来发展空间的领域,如对话QA系统,增强QA系统的认知能力等。
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