The Research Of A Textual Semantic Comprehension Model Based On Human Cognitive Process (HTSC)

With the rapid development of the Web,online text information tends to be volume,velocity,variety and value,which make Web users always be lost on the Web.Under this situation,it requires a machine-oriented textual semantic comprehension method to help users efficiently organize and manage those massive textual information,and then to offer proper Web services.However,there are some problems that the current textual semantic comprehension methods are facing,including:(1)there is a great gap between the cognitive ability of humans and the comprehensive ability of machines;(2)the contradiction between the high complexity of comprehension and the volume feature of the texts on the Web;(3)the comprehension process lacks of the guidance of the background knowledge.To deal with the above problems,this paper proposes a textual semantic comprehension model based on human cognitive process(HTSC),whose basic idea is “cognitive framework + implementation with lightweight algorithms”.Through mimicking human cognitive process,the comprehension results of HTSC are much close to the results of human cognition,which is properly to solve problem(1);Implementation with lightweight algorithms makes HTSC model capable of dealing with large-scale online texts,which is properly to solve problem(2);HTSC model supports the dynamic interactions between the current text and the background knowledge,which is properly to solve problem(3).The main research work of this paper is as below:1.On the basis of human memory process and text information processing,proposing the HTSC model that is comprised of sensory memory,perceptual associative memory,working memory,transient episodic memory and long-term memory,so as to provide theoretical support for machine-oriented textual semantic comprehension.2.Under the framework of HTSC model,proposing textual semantic representations in different memory systems,so as to provide semantical carriers for machine-oriented textual semantic comprehension,including: working memory based multi-level textual semantic representation;transient episodic memory and long-term memory based textual semantic representation,which is composed of three levels: word,discourse and topic;power series representation model is proposed based on human concept learning.3.Based on the valency theory in linguistics,proposing a semantical relation mining algorithm between word,which avoids the defects of weak semantics and strong dependency on parameters carried by traditional relation mining algorithms.This part of work including: on the basis of valency theory and the restrict of memory capacity,proposing the verb dependency set mining algorithm;based on verb dependency set and association relation mining algorithm,proposing the elemental relation mining algorithm between words;combined with verb dependency set and elemental relations,proposing the specific relation mining algorithm.4.Inspired by human cognitive process,proposing a method for dynamic comprehension of textual semantics,so as to make the comprehension results much close to human cognition.Works including: based on the theory of connected graph,giving formalized definition of coherence interruptions of textual semantics;based on coherence interruptions of textual semantics,proposing the generation method of activation cue;under semantic link network,proposing the activation method of background knowledge and its fusion algorithm.5.On the basis of human concept learning,proposing the complexity measurement of textual semantics,which is capable of providing personalized services for users having different background knowledge.Works including: in view of the algebraic complexity of concept,proposing the algebraic complexity measurement of text,ACT;through analyses of those facts that have influences on the complexity measurement,giving the complexity measurement for words and relations;on the basis of the complexity measurement of words and relations,proposing two kinds of expanding methods for ACT,which are extension of algebraic complexity of text,EACT,and general extension of algebraic complexity of text,GEACT.6.Under the theory of HTSC model and its complexity measurements,developing two application systems: cognitive process based intelligent interaction system and complexity measurement based search engine demo,which provide real,suitable application scenarios for our proposed HTSC model.This paper is willing to discover the theory and methodology of machine-oriented textual semantic comprehension,by the means of human cognitive process.It gives theoretical analyses and experiments on textual semantic representation,semantic relation discovery between words,dynamic interaction algorithm between the current text and the background knowledge,and complexity measurement.The research achievement can be directly applied to semantic search,personalized recommendation,relations discovery between users and online public sentiment monitoring,etc.