Abstract:

With the increasing maturity of the networked social life, many fields such as research and commerce have encountered the problems of processing Chinese texts. Parsing

the texts from all aspects is necessary for the increasingly deepening research of text classification. On the one hand, semantic parsing is essential to text mining. On the

other hand, context recognition can be widely applied in numerous text mining problems, such as sentiment analysis, public feeling analysis and so on. In this paper, a context

classification model is proposed based on syntactic decision trees, Ngram feature extraction and SVM classifiers to recognize the different meanings of the same words under

different short contexts. The results show that the proposed model, which can batch process data, has favorable generalization ability, indicating that the model can solve the

problems of context recognition efficiently.

Key words:  , Chinese text processing； context identification； decision tree； Ngram model； SVM classifier

CLC Number: 

• TP393