[Objective] This paper improves the methods of text classification based on VSM using semantic increment, and the model is verified by experiments. [Methods] Combing the studies of semantic vector and its improvement in text representation, this paper improves VSM based on semantic increment, and proposes an implementation frame of semantic vector representation of texts. Furthermore, based on the mapping relationships between words and concepts in domain Ontology, the construction of concept hierarchy tree and words positioning are constructed, semantic similarity of concepts is calculated, and the semantic vector model of texts' representation is achieved. [Results] The comparative experiments of texts classification demonstrate that the proposed method is feasible and effective, and the performance of this method is better than traditional methods from the perspectives of Precison, Recall and F1-Measure. [Limitations] The description of text semantic information is not good enough, and it is necessary to explore the authentic semantic methods in text modeling. In addition, more comparative experiments on several datasets should be conducted in order to obtain more accurate results. [Conclusions] The semantic improvement on traditional VSM is explored which is important for further text classification and semantic association.
胡吉明, 肖璐. 向量空间模型文本建模的语义增量化改进研究[J]. 现代图书情报技术, 2014, 30(10): 49-55.
Hu Jiming, Xiao Lu. Semantic Incremental Improvement on Vector Space Model for Text Modeling. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2014.10.08.
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