A Topic-Oriented Syntactic Component Extraction Model for Social Media
Topic-oriented understanding is to extract information from various language instances, which reflects the characteristics or trends of semantic information related to the topic via statistical analysis. The syntax analysis and modeling is the basis of such work. Traditional syntactic formalization approaches widely used in natural language understanding could not be simply applied to the text modeling in the context of topic-oriented understanding. In this paper, we review the information extraction mode, and summarize its inherent relationship with the “Subject- Predicate” syntactic structure in Aryan language. And we propose a syntactic element extraction model based on the “topic-description” structure, which contains six kinds of core elements, satisfying the desired requirement for topic-oriented understanding. This paper also describes the model composition, the theoretical framework of understanding process, the extraction method of syntactic components, and the prototype system of generating syntax diagrams. The proposed model is evaluated on the Reuters 21578 and SocialCom2009 data sets, and the results show that the recall and precision of syntactic component extraction are up to 93.9% and 88%, respectively, which further justifies the feasibility of generating syntactic component through the word dependencies.
KeywordsText Understanding Topic-oriented Parsing Syntactic Component Extraction Text Modeling Natural Language Understanding
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