基于深度神经网络的法语命名实体识别模型

现有法语命名实体识别(NER)研究中,机器学习模型多使用词的字符形态特征,多语言通用命名实体模型使用字词嵌入代表的语义特征,都没有综合考虑语义、字符形态和语法特征。针对上述不足,设计了一种基于深度神经网络的法语命名实体识别模型CGC-fr。首先从文本中提取单词的词嵌入、字符嵌入和语法特征向量;然后由卷积神经网络(CNN)从单词的字符嵌入序列中提取单词的字符特征;最后通过双向门控循环神经网络(BiGRU)和条件随机场(CRF)分类器根据词嵌入、字符特征和语法特征向量识别出法语文本中的命名实体。实验中,CGC-fr在测试集的F1值能够达到82.16%,相对于机器学习模型NERC-fr、多语言通用的神经网络模型LSTM-CRF和Char attention模型,分别提升了5.67、1.79和1.06个百分点。实验结果表明,融合三种特征的CGC-fr模型比其他模型更具有优势。

Recognition model for French named entities based on deep neural network

In the existing French Named Entity Recognition (NER) research, the machine learning models mostly use the character morphological features of words, and the multilingual generic named entity models use the semantic features represented by word embedding, both without taking into account the semantic, character morphological and grammatical features comprehensively. Aiming at this shortcoming, a deep neural network based model CGC-fr was designed to recognize French named entity. Firstly, word embedding, character embedding and grammar feature vector were extracted from the text. Then, character feature was extracted from the character embedding sequence of words by using Convolution Neural Network (CNN). Finally, Bi-directional Gated Recurrent Unit Network (BiGRU) and Conditional Random Field (CRF) were used to label named entities in French text according to word embedding, character feature and grammar feature vector. In the experiments, F1 value of CGC-fr model can reach 82.16% in the test set, which is 5.67 percentage points, 1.79 percentage points and 1.06 percentage points higher than that of NERC-fr, LSTM(Long Short-Term Memory network)-CRF and Char attention models respectively. The experimental results show that CGC-fr model with three features is more advantageous than the others.