融合全局词语边界特征的中文命名实体识别方法

Chinese Named Entity RecognitionIncorporating Global Word Boundary Features

目前在中文命名实体识别的任务中经常采用有监督的字序列标注模型。我们在实际应用中发现,基于字序列标注模型的中文命名实体识别模型对于词语边界的识别错误是影响识别效果的主要因素之一,边界错误平均占错误结果中的47.5%。该文通过在平均感知机模型中引入全局的词语边界特征,使得人名、地名、机构名识别的F值平均提升了0.04并降低了边界错误占错误结果的比例。

Supervised character sequence labeling model is a popular method in Chinese named entity recognition （NER） task. It is found in practice suffering from word boundary error, covering roughly 47.5% of all errors. This paper incorporates global words boundary features in averaged perceptron model. Experiments indicate that the F value of recognizing people name, location names and organization names is improved by 0.04, reducing the proportion of boundary errors in overall errors.