摘要： 在自然语言处理领域中，对于泰语等东南亚语言的分句处理是一项具有挑战性的工作。将序列标注模型应用于句子切分任务，提出基于双向长短期记忆循环神经网络的句子边界自动识别模型。利用Glove词向量技术，将泰语句子中的词或字转换为不同维度的向量，进而将词或字向量组合成为句子向量输入模型进行训练。在此基础上，通过双向网络结构捕捉上下文信息以达到更好的句子切分效果。实验结果表明，该模型在泰语句子切分任务上表现出非常精准的识别效果。

关键词: 自然语言处理, 句子切分, 深度学习, 循环神经网络, 长短期记忆网络, 泰语

Abstract: In the field of Natural Language Processing,clause processing of Southeast Asian languages such as Thai is a challenging task.Therefore,sequence tagging model is applied to sentence segmentation and a sentence boundary automatic recognition model based on bidirectional Long Short-Term Memory cycle neural network is proposed.The words or characters in Thai sentences are transformed into vectors with different dimensions by using Glove word vector technology,and then the word vectors or character vectors are combined into a sentence vector and are input into the model for training.On this basis,the context information is captured through the bidirectional network structure to achieve better sentence segmentation effect.The experimental results show that the model is very accurate in the task of sentence segmentation in Thai.

Key words: Natural Language Processing(NLP), sentence segmentation, deep learning, Recurrent Neural Networks(RNN), Long Short-Term Memory(LSTM) network, Thai

中图分类号: 

• TP391.1