《知网》在命名实体识别中的应用研究

A Research on the Application of HowNet in Named Entity Recognition

命名实体识别是自然语言处理领域的一项基础研究,它对于语言的深层处理有重要意义。该文以最大熵模型为基础来进行名实体识别,提出了基于《知网》的两种改进策略来增强模型的泛化性能。第一种策略是将《知网》中词的义原作为特征加入到最大熵模型中;第二种策略是利用《知网》来计算最大熵模型中词特征之间的概念相似度。在北京大学《人民日报》语料上的实验结果表明第一种策略可以有效地提高名实体识别的性能,第二种策略的改进效果不明显。

Named entity recognition is a foundational issue of natural language processing and of substantial significance to deep language processing. This work adopts the maximum entropy model for named entity recognition and proposes two improvement strategies based on HowNet to enhance the generalization of maximum entropy model. The first strategy is to add the HowNet＇s sememe of concepts into the maximum entropy model as features. The other is to take advantage of HowNet to calculate the similarity between word features in maximum entropy model. The experiments on China Daily corpus show that the first strategy could improve named entity recognition performance significantly, while the second improves the performance trivially.