New Approaches for Natural Language Understanding based on the Idea that Natural Language encodes both Information and its Processing Procedures

TL;DR

This paper proposes a novel approach to natural language understanding by recognizing that language encodes both information and processing procedures, enabling machines to better interpret dialogue and complex information structures.

Contribution

It introduces a classification system for language chunks and a hierarchical information architecture, providing a new theoretical and practical basis for NLU in AI.

Findings

Validated approach with examples showing improved understanding

Developed classification encoding system for language data

Applicable to large-scale AI information processing

Abstract

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.