基于SVM和泛化模板协作的藏语人物属性抽取

Tibetan Person Attribute Extraction Based on SVM and Pattern

该文提出了一种基于SVM和泛化模板协作的藏语人物属性抽取方法。该方法首先构建了基于藏语语言规则的模板系统,收集了包括格助词、特殊动词等具有明显语义信息的特征建设模板并泛化。针对规则方法的局限性,该文在模板的基础上,采用SVM机器学习方法,设计了一种处理多分类问题的层次分类器结构,同时对多样化的特征选取给予说明。最后,实验结果表明,基于SVM和模板相结合的方式可以对人物属性抽取的性能有较大提高。

This paper proposes an SVM and pattern based approach to Tibetan person attribute extraction. The pattern system is built with language rules on Tibetan language features with clear semantic information, such as case auxiliary words, particular verb and etc. Then, a machine learning approach via SVM is introduced to build a a hierarchy classifier strategy. Experiment results indicate a significant improvement in person attributes extraction.