First of all, please URLet all you knew about the lexical classification, then let's jump to the conclusion. This paper reclassified lexical chunks into data chunks, structure chunks, and pointer chunks. Almost all data chunks are information sets. According to the difference of the set structures, data chunks can be further divided into attribute chunks and entity chunks. According to the different abstraction level and method, attribute chunks can be further divided into basic attribute chunks, extended attribute chunks, and advanced attribute chunks. All of the above classification principles are structural and functionalbased discrimination, instead of artificially divide lexical chunks into a noun, adjective, pronouns, and so on. Now, let's back to the normal study process. The author believes natural language is one of the ways information is encoded and it has highly abstracted and conceptualized the information . Therefore the study begins with disassembling the information represented by natural language and then discovered the classification coding system of attribute information, and the abstraction relations between attribute information and entities in the real world. To have a clear and better discussion , the author constructed corresponding data storage models, and extract three kinds of data reading modes on those data storage models, they are the defining reading mode which is driven by the structural word: be, the set reading mode which is driven by the structural word: have, and the process reading mode which is driven by verbs. Sentences output by the above data reading modes can be further divided into the data description task, the data verification task, and the data search task, according to task types represented by these sentences .. .
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 URLanization 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 .
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First of all, please URLet all you knew about the lexical classification, then let's jump to the conclusion. This paper
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
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into data chunks, structure chunks, and pointer chunks. Almost all data chunks are
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 URLanization architecture (including constitutional structures of
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sets. According to the difference of the set structures, data chunks can be further divided into attribute chunks and entity chunks. According to the different abstraction level and method, attribute chunks can be further divided into basic attribute chunks, extended attribute chunks, and advanced attribute chunks. All of the above classification principles are structural and functionalbased discrimination, instead of artificially divide lexical chunks into a noun, adjective, pronouns, and so on. Now, let's back to the normal study process. The author believes natural language is one of the ways information is encoded and it has highly abstracted and conceptualized the
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
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. Therefore the study begins with disassembling the information represented by natural language and then discovered the classification coding system of attribute information, and the abstraction relations between attribute information and entities in the real world. To have a clear and better discussion
conveyed in the dialogue. In section 4
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constructed corresponding data storage models, and extract three kinds of data reading modes on those data storage models, they are the defining reading mode which is driven by the structural word: be, the set reading mode which is driven by the structural word: have, and the process reading mode which is driven by verbs. Sentences output by the above data reading modes can be further divided into the data description task, the data verification task, and the data search task, according to task types represented by these sentences ..
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