Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (7): 1879-1883.DOI: 10.11772/j.issn.1001-9081.2019111965

• Artificial intelligence • Previous Articles     Next Articles

News named entity recognition and sentiment classification based on attention-based bi-directional long short-term memory neural network and conditional random field

HU Tiantian1, DAN Yabo2, HU Jie3, LI Xiang2, LI Shaobo2   

  1. 1. School of Computer Science and Technology, Guizhou University, Guiyang Guizhou 550025, China;
    2. School of Mechanical Engineering, Guizhou University, Guiyang Guizhou 550025, China;
    3. College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang Guizhou 550025, China
  • Received:2019-11-18 Revised:2020-01-05 Online:2020-07-10 Published:2020-06-29
  • Supported by:
    This work is partially supported by the Major Research Program Cultivation Project of the National Natural Science Foundation of China (91746116).

基于注意力机制的Bi-LSTM结合CRF的新闻命名实体识别及其情感分类

胡甜甜1, 但雅波2, 胡杰3, 李想2, 李少波2   

  1. 1. 贵州大学 计算机科学与技术学院, 贵阳 550025;
    2. 贵州大学 机械工程学院, 贵阳 550025;
    3. 贵州财经大学 大数据统计学院, 贵阳 550025
  • 通讯作者: 胡甜甜
  • 作者简介:胡甜甜(1993-),女,湖南岳阳人,硕士研究生,CCF会员,主要研究方向:自然语言处理、大数据挖掘;但雅波(1993-),男,湖北天门人,硕士研究生,主要研究方向:材料信息学、大数据挖掘;胡杰(1990-),男,湖南岳阳人,副教授,博士,主要研究方向:自然语言处理;李想(1994-),男,重庆人,硕士研究生,主要研究方向:材料信息学;李少波(1973-),男,湖南岳阳人,教授,博士,主要研究方向:智能制造、大数据挖掘。
  • 基金资助:
    国家自然科学基金重大研究计划培育项目(91746116)。

Abstract: Attention-based Bi-directional Long Short-Term Memory neural network and Conditional Random Field (AttBi-LSTM-CRF) model was proposed for the corpus core entity recognition and core entity sentiment analysis task of Sohu coreEntityEmotion_train. Firstly, the text was pre-trained, each word was mapped into a low-dimensional vector with the same dimension. Then, these vectors were input into the Attention-based Bi-directional Long Short-Term Memory neural network (AttBi-LSTM) to obtain the long-term context information and focus on the information highly related to the output label. Finally, the optimal label of the entire sequence was obtained through the Conditional Random Field (CRF) layer. The comparison experiments were conducted among AttBi-LSTM-CRF model, Bi-directional Long Short-Term Memory neural network (Bi-LSTM), AttBi-LSTM and Bi-directional Long Short-Term Memory neural network and Conditional Random Field (Bi-LSTM-CRF) model. The experimental results show that, the accuracy of AttBi-LSTM-CRF model is 0.78, the recall is 0.667, and the F1 value is 0.553, which are better than those of the comparison models. The superiority of AttBi-LSTM-CRF performance is verified.

Key words: core entity recognition, sentiment classification, Conditional Random Field (CRF), attention mechanism, Bi-directional Long Short-Term Memory neural network (Bi-LSTM)

摘要: 针对搜狐coreEntityEmotion_train语料核心实体识别和核心实体情感分析的任务,提出了基于注意力机制的长短期记忆神经网络结合条件随机场模型(AttBi-LSTM-CRF)。首先,对文本进行预训练,将每个字映射为维度相同的低维向量;然后,把这些向量输入到基于注意力机制的长短期记忆神经网络(AttBi-LSTM)中,以获取长远的上下文信息并集中注意力到与输出标签高度相关的信息上;最后,通过条件随机场(CRF)层获取整个序列的最优标签。将AttBi-LSTM-CRF模型与双向长短记忆神经网络(Bi-LSTM)、AttBi-LSTM和双向长短期记忆神经网络结合条件随机场(Bi-LSTM-CRF)模型进行对比实验。实验结果表明,AttBi-LSTM-CRF模型的准确率达到0.786,召回率达到0.756,F1值达到0.771,优于对比模型,验证了AttBi-LSTM-CRF性能的优越性。

关键词: 核心实体识别, 情感分类, 条件随机场, 注意力机制, 双向长短期记忆神经网络

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