Abstract
Natural language understanding (NLU) focusing on machine reading comprehension is a branch of natural language processing (NLP). The domain of the developing NLU system covers from sentence decoding to text understanding and the automatic decoding of GP sentence belongs to the domain of NLU system. GP sentence is a special linguistic phenomenon in which processing breakdown and backtracking are two key features. If the syntax-based system can present the special features of GP sentence and decode GP sentence completely and perfectly, NLU system can improve the effectiveness and develop the understanding skill greatly. On the one hand, by means of showing Octav Popescu’s model of NLU system, we argue that the emphasis on the integration of syntactic, semantic and cognitive backgrounds in system is necessary. On the other hand, we focus on the programming skill of IF-THEN-ELSE statement used in N-S flowchart and highlight the function of context free grammar (CFG) created to decode GP sentence. On the basis of example-based analysis, we reach the conclusion that syntax-based machine comprehension is technically feasible and semantically acceptable, and that N-S flowchart and CFG can help NLU system present the decoding procedure of GP sentence successfully. In short, syntax-based NLU system can bring a deeper understanding of GP sentence and thus paves the way for further development of syntax-based natural language processing and artificial intelligence.
Highlights
Natural language understanding (NLU), speech segmentation, text segmentation, part-of-speech tagging, word sense disambiguation, syntactic ambiguity, etc. come under the umbrella term “natural language processing (NLP)”.[1]
The development of NLU is briefly traced from the early years of machine translation to today's question answering and translation systems. [2,3]NLU today deals with machine reading comprehension in artificial intelligence (AI) [4]and is applied to a diverse set of computer applications.[5,6]
[12] For example, the fact is established that abduction rather than deduction is generally viewed as a promising way to apply reasoning in NLU. [13]The development of NLU can focus on the design of a stochastic model topology that is optimally adapted in quality and complexity to the task model and the available training data.[14]
Summary
Natural language understanding (NLU), speech segmentation, text segmentation, part-of-speech tagging, word sense disambiguation, syntactic ambiguity, etc. come under the umbrella term “natural language processing (NLP)”.[1]. Natural language understanding (NLU), speech segmentation, text segmentation, part-of-speech tagging, word sense disambiguation, syntactic ambiguity, etc. Syntax-supported systems attempt to help machine to get a deeper understanding of garden path sentence and the related algorithms deserve special attention in the future of NLU developing. The automatic decoding of GP sentence is a challenge for NLU systems for machine has to have access to grammatical, semantic and cognitive knowledge in order to understand natural language smoothly as human brains do.
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