基于组合深度模型的现代汉语数量名短语识别

Quantity Noun Phrase Structure Recognition Based on Combined Deep Learning Model

数量名短语的识别是识别由数量短语修饰的名词短语左右边界的研究.以往研究中,基于统计学习模型的数量短语识别方法依赖人工特征,需要通过专家知识构建知识库来实现对“数词+量词”短语的识别.本文在以往研究基础上纳入“名词”形成“数词+量词+名词”等八类数量名短语,并采用深度学习方法解决这一边界识别任务.通过BERT模型对原始文本进行上下文特征表示,利用Lattice LSTM模型字词结合的思想将标准分词作为软特征融入文本字符级的特征表示中,最后通过CRF全局约束识别数量名短语边界.实验结果表明,本文方法在AMR语料上达到较优结果,精确率、召回率、F1值分别为80.83%,89.78%,85.07%.

The research on recognition of quantity noun phrases is the identity of the left and right boundaries of quantity noun phrases.In previous studies,this task focuses on the recognition of quantity phrase and relies on artifical features which are constructed by experts based on statistical learning models.In this paper,we aim at the recognition of quantity noun phrases which have 8 subtypes and propose a neural network model to address the issue.Firstly,BERT is used to represent the contextual features of the original text.Then,the standard word segmentation is incorporated into the feature representation of the text character level as a soft feature by using the idea of Lattice LSTM model.Finally,the left and right boundaries of the“quantity noun phrase”are identified by the CRF global constraint.The experimental results show that this method achieves the better results and the precision,recall and F1 value reaches 80.83%,89.78%,85.07%respectively in the corpus of CAMR.