Research Advances and Prospect of Recognizing Textual Entailment and Knowledge Acquisition

Textual entailment, as a directional semantic reasoning relation, is widely distributed in natural language texts. Research on textual entailment is a fundamental study in the field of natural language processing. With various applications, it is helpful to other natural language processing tasks. This paper clarifies the scope of textual entailment at first. As a binary relationship, textual entailment has three basic research tasks, that is, recognizing textual entailment, knowledge acquisition and generating entailment pairs. There are two key problems in recognizing textual entailment, that is, semantic representation and reasoning mechanism. There are also two key problems in knowledge acquisition, that is, knowledge representation and knowledge source. This paper makes a detailed analysis on the internal and external factors leading to the slow process of research on generating entailment pairs. This paper focuses on these key problems while expounding methods of recognizing textual entailment and knowledge acquisition. This paper points out the pros and cons of each method then. The development of research on textual entailment is inseparable with international evaluation exercises. This paper summarizes the datasets from these evaluation exercises. The arrival of the big data era and the development of deep learning theory bring a new rich source of knowledge and powerful tools, as well as novel research topics. The future research directions are pointed out and their feasibility is also discussed under the current research situation. © 2017, Science Press. All right reserved.