计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 223-230.doi: 10.11896/jsjkx.210100046
所属专题: 自然语言处理 虚拟专题
丁锋, 孙晓
DING Feng, SUN Xiao
摘要: 基于方面情感分析( Aspect-Based Sentiment Analysis,ABSA)是自然语言处理的热门课题,其中意见目标抽取和意见目标情感极性分类是ABSA的基本子任务之一。而很少有研究直接抽取特定情感极性的意见目标,尤其是抽取更有潜在价值的消极情绪意见目标。文中提出了一种全新的ABSA子任务--抽取消极情绪意见目标(Negative-Emotion Opinion Target Extraction,NE-OTE),并提出了基于注意力机制和单词与字符混合嵌入的BiLSTM-CRF模型(Attention-based BiLSTM-CRF with Word Embedding and Character Embedding,AB-CE),在双向长短时记忆网络(Bi-directional Long Short-Term Memory,BiLSTM)学习文本语义信息和捕获长距离双向语义依赖关系的基础上,通过注意力机制使模型更好地关注输入序列中的关键部分和捕获与意见目标及其情感倾向相关的隐含特征,最终通过CRF层预测句子级别的全局最佳标签序列,实现对消极情绪意见目标的抽取。文中基于主流ABSA任务基准数据集构建了3个NE-OTE任务数据集,并在这些数据集上进行了广泛的实验,实验结果显示,所提模型能够有效识别消极情绪意见目标,且识别效果明显优于其他基线模型,验证了所提方法的有效性。
中图分类号:
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