基于细粒度词表示的命名实体识别研究

命名实体识别(NER)是自然语言处理中的一项基础任务,其性能的优劣极大地影响着关系抽取、语义角色标注等后续任务。传统的统计模型特征设计难度大、领域适应性差,一些神经网络模型则忽略了词本身所具有的形态学信息。针对上述问题,该文构建了一种基于细粒度词表示的端到端模型(Finger-BiLSTM-CRF)来进行命名实体识别任务。该文首先提出一种基于注意力机制的字符级词表示模型Finger来融合形态学信息和单词的字符信息,然后将Finger与BiLSTM-CRF模型联合进行实体识别,最终该方法以端到端、无任何特征工程的方式在CoNLL 2003 数据集上取得了F₁为91.09%的结果。实验表明,该文设计的Finger模型显著提升NER系统的召回率,从而使得模型的识别能力显著提升。

Named Entity Identification Based on Fine-Grained Word Representation

Named entity recognition(NER), whose performance has a highly marked impact onthe following piped nature language processing(NLP) system such as relation extraction and semantic role labeling, is a fundamental stage in NLP. Traditional statistical models have difficulty infeature designing, whose features have poor cross-domain adaptability, and some neural network models neglectmorphological information of the word.Aiming at the above problems, our paper proposes a new end-to-end neural network model(Finger-BiLSTM-CRF) based on a fine-grained word representation for named entity recognition task. First, we design Finger, a character-level word representation model based on the attention mechanism, for the integration of morphological information with information from each character of current token. Secondly, we combine Finger with BiLSTM-CRF for named entity recognition task. Finally, the model trained in an end-to-end fashion achieves a F1 score of 91.09% on test dataset for CoNLL 2003. The experimental results show that our Finger model significantly boosts the recall of the NER system , which results in performance improvement of recognition ability of the system.