A Formal Logic Approach to Chinese Recognizing Textual Entailment
Date Issued
2014
Date
2014
Author(s)
Chang, Fu-Chieh
Abstract
In the research of natural language processing (NLP), understanding the natural language is always a challenging problem. Traditionally, the research of NLP focuses on the semantics and logic of natural language. However, the present NLP research trend is focusing on the big data and machine learning techniques. These two methods have their own pros and cons; however, the traditional research of semantics and logic are seldom discussed in the recent works, and the existing machine learning techniques also have their limitations. Combining the traditional works on semantics with machine learning techniques is a good perspective to research.
We build a system to solve the Chinese recognizing textual entailment (RTE) problem by formal logic method. Based on the theory of formal semantics and computational semantics, first, we use the machine learning technique to convert Chinese sentences in natural language into syntax trees. Then, we propose an algorithm to convert the syntax trees into semantic representations. Also, we propose a method that solves the RTE problem by integrating external knowledge resources with the proposed semantic representations. With these semantic representations, we can use the theorem proving techniques to solve the problem of Chinese RTE. Then, we demonstrate that our approach can solve some simple cases of Chinese RTE. Also, we show the possibilities and difficulties to solve the real-world cases. Finally, we point out the strengths and weaknesses of our system, and the possibilities on future research to improve our system.
Subjects
形式語意學
計算語意學
自然語言理解
一階邏輯
中文文本蘊含辨識
Type
thesis
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