一种局部与全局特征相结合的主题域识别模型

传统的主题域识别技术主要局限于单一领域，缺乏领域间的交互式协同，难以保证识别结果的准确性，因此提出一种局部与全局特征相结合的主题域识别模型.该模型一方面基于实体在领域内的局部特征进行局部识别，另一方面基于领域间协同作用、领域相关度等全局特征对各个局部识别结果进行一致化趋近，从而使识别结果更全面、有效.另外，针对相似矩阵的更新时机、协同作用的量化以及迭代终止条件的设定三个方面对主题域识别算法进行了优化.通过实验验证了本文所提出的关键技术的可行性和有效性.

A Topic Domain Identification Model Combining Local and Global Characteristics

Traditional identification techniques focus on a single domain and lack the mutual collaboration among different domains， which often lead to dumb results. So， a topic domain identification model combining local and global characteristics is proposed.Local identification is performed based on entities’ local characteristics within one domain. On the other hand， these local identification results tend to be consistent with each other based on the global characteristics such as the collaboration and relevance among domains， which can maintain the accuracy of identification effectively. In addition， some improvements are made for the algorithm of topic domain identification， including similarity matrix updating， collaboration quantifying and iteration terminating. The experiments demonstrate the feasibility and effectiveness of the proposed model.