An ASP Based Approach to Answering Questions for Natural Language Text
An ASP Based Approach to Answering Questions for Natural Language Text
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基于 ASP 的自然语言文本问答方法
DOI：
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发表时间：
2018
期刊：
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通讯作者：
G. Gupta
中科院分区：
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作者：
Dhruva Pendharkar;G. Gupta
提出了一种基于答案集编程（ASP）的自然语言文本知识表示方法。文本中的知识使用新戴维森式的形式主义建模，表示为答案集程序。相关的常识知识还从WordNet等资源中导入，并在ASP中表示。由此产生的知识库，然后可以用来执行推理的帮助下，ASP系统。这种方法可以促进许多自然语言任务，如自动问答，文本摘要和自动问题生成。基于ASP的技术表示，如默认推理、层次知识组织、默认偏好等，用于对完成这些任务所需的常识推理方法进行建模。在本文中，我们描述了CASPR系统，我们已经开发出自动化的任务，回答自然语言问题的英文文本。CASPR可以被视为一个通过“理解”文本来回答问题的系统，并已在SQuAD数据集上进行了测试，结果令人鼓舞。
An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language text. Knowledge in the text is modeled using a Neo Davidsonian-like formalism, represented as an answer set program. Relevant common sense knowledge is additionally imported from resources such as WordNet and represented in ASP. The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system. This approach can facilitate many natural language tasks such as automated question answering, text summarization, and automated question generation. ASP-based representation of techniques such as default reasoning, hierarchical knowledge organization, preferences over defaults, etc., are used to model common-sense reasoning methods required to accomplish these tasks. In this paper we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. CASPR can be regarded as a system that answers questions by “understanding” the text and has been tested on the SQuAD data set, with promising results.