Representing unstructured text semantics for reasoning purpose

Moteshakker Arani Zohre; Abdollahzadeh Barforoush Ahmad; Shirazi Hossein

摘要

To interpret a natural language text using a machine, we need to convert its semantics into structured information. In the field of Natural Language Processing, multiple tasks have been designed and developed to interpret the semantics of an unstructured text, and change words into meanings. However, there are some challenges in directly using the output of these tasks in subsequent applications such as logical inference. There has been a growing interest in building and enhancing state-of-the-art semantic representation systems in recent years. However, most of these systems involve supervised models that benefit from manually annotated data, which is not accessible for a wide range of languages. This paper presents a new framework for modeling text in order to extract its information, and through an inference system, obtain new information that is not explicitly stated in the text, but could be logically inferred. This framework is based on Open Information Extraction and Semantic Web techniques for machine reading. We translate the text into a machine-readable representation by using Semantic Types Identification and Question-based Semantic Role Labeling, which could be used in low-resource languages. We integrate the extracted information into the background knowledge by using existing Semantic Web standards. The proposed framework could increase generalization of labelling and reduce ambiguities, therefore, it is an appropriate solution for preparing text for reasoning systems.

机译：要使用机器解释自然语言文本，我们需要将其语义转换为结构化信息。在自然语言处理领域，已经设计并开发了多个任务以解释非结构化文本的语义，并将文字变为含义。但是，在随后的应用程序中使用这些任务的输出直接存在一些挑战，例如逻辑推断。近年来，在建设和增强最先进的语义代表系统方面越来越感兴趣。然而，这些系统中的大多数涉及受益于手动注释数据的监督模型，这对于各种语言无法访问。本文介绍了用于建模文本的新框架，以便提取其信息，并通过推理系统，获取文本中未明确说明的新信息，但可以在逻辑上推断。该框架是基于开放信息提取和机器读取的语义网络技术。通过使用语义类型标识和基于问题的语义角色标记，我们将文本转换为机器可读表示，可以用于低资源语言。我们通过使用现有的语义Web标准将提取的信息集成到背景知识中。拟议的框架可以提高标签的概括并减少含糊不清，因此，它是制备推理系统的文本的适当解决方案。

Representing unstructured text semantics for reasoning purpose

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