论文标题
基于自然语言编码信息及其处理程序的想法的自然语言理解的新方法
New Approaches for Natural Language Understanding based on the Idea that Natural Language encodes both Information and its Processing Procedures
论文作者
论文摘要
我们必须认识到,自然语言是一种信息编码的方式,它不仅编码信息处理方式,而且还编码了如何处理信息的过程。为了理解自然语言,与我们构想和设计计算机语言一样,第一步是分开信息(或数据)和信息的处理过程(或数据)。在自然语言中,一些数据的处理过程直接编码为结构块和指针块(本文将词汇块重新分类为数据块,结构块和指针块);一些数据的处理程序暗示句子结构中;一些处理程序的请求由信息发送者表示,并由信息接收器处理。对于数据部分,讨论了属性信息的分类系统和信息组织体系结构(包括信息集的宪法结构和信息集之间的层次结构)。在第2节中,第2节中详细阐述的理论部分已在示例中进行了验证,并证明了本文中的研究实现了使机器能够理解对话中传达的信息的目的。在第4节中,作者总结了“理解”的基本条件,重新考虑了“理解”以及如何进行。本文中的研究为NLU提供了一种实用的理论基础和研究方法。它也可以在人工智能(AI)区域中的大规模和多类信息处理中应用。
We must recognize that natural language is a way of information encoding, and it encodes not only the information but also the procedures for how information is processed. To understand natural language, the same as we conceive and design computer languages, the first step is to separate information (or data) and the processing procedures of information (or data). In natural language, some processing procedures of data are encoded directly as the structure chunk and the pointer chunk (this paper has reclassified lexical chunks as the data chunk, structure chunk, and the pointer chunk); some processing procedures of data imply in sentences structures; some requests of processing procedures are expressed by information senders and processed by information receivers. For the data parts, the classification encoding system of attribute information and the information organization architecture (including constitutional structures of information sets and the hierarchy between the information sets) were discussed. In section 2, the theoretical part elaborated in section 2 has been verified in examples and proofed that the studies in this paper have achieved the goal of enabling machines to understand the information conveyed in the dialogue. In section 4, the author summarizes the basic conditions of "Understanding", rethinks what "Understanding" is and how to proceed. The study in this paper provides a practical, theoretical basis and research methods for NLU. It also can be applied in large-scale and multi-type information processing in the artificial intelligence (AI) area.