基于神经网络的中文谓语动词识别研究

识别谓语动词是理解句子的关键。由于中文谓语动词结构复杂、使用灵活、形式多变，识别谓语动词在中文自然语言处理中是一项具有挑战的任务。本文从信息抽取角度，介绍了与中文谓语动词识别相关的概念，提出了一种针对中文谓语动词标注方法。在此基础上，研究了一种基于Attentional-BiLSTM-CRF神经网络的中文谓语动词识别方法。该方法通过双向递归神经网络获取句子内部的依赖关系，然后用注意力机制建模句子的焦点角色。最后通过条件随机场（Conditional random field, CRF)层返回一条最大化的标注路径。此外，为解决谓语动词输出唯一性的问题，提出了一种基于卷积神经网络的谓语动词唯一性识别模型。通过实验，该算法超出传统的序列标注模型CRF，在本文标注的中文谓语动词数据上到达76.75%的F值。

Research on Chinese Predicate Verb Recognition Based on Neural Network

Recognizing predicate verbs is the key to understanding sentences. Because Chinese predicate verbs are complex in structure, flexible in use, and changeable in form, identifying predicate verbs is a challenging task in Chinese natural language processing. This article introduces the concepts related to the recognition of Chinese predicate verbs from the perspective of information extraction, and proposes a method for marking Chinese predicate verbs. On this basis, a Chinese predicate verb recognition method based on Attentional-BiLSTM-CRF neural network is studied. This method uses the bidirectional recurrent neural network to obtain the dependency relationship within the sentence, and then uses the attention mechanism to model the focus role of the sentence. Finally, a maximized labeling path through the conditional random field(CRF)layer is returned. In addition, in order to solve the problem of the uniqueness of predicate verb output, a unique recognition model of predicate verb based on convolutional neural network is proposed. Through experiments, the algorithm exceeds the traditional sequence labeling model CRF, and reaches an F value of 76.75% on the Chinese predicate verb data labeled in this paper.