论文标题
增强利用本地上下文嵌入的细粒情感分类
Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding
论文作者
论文摘要
面向目标的情感分类是自然语言处理的精细任务，以分析目标的情感极性。为了提高情感分类的表现，许多方法提出了各种注意机制，以捕获目标的重要上下文单词。但是，以前的方法忽略了目标情绪及其本地环境的显着相关性。本文提出了一个本地上下文感知网络（LCA-net），该网络配备了局部上下文嵌入和本地上下文预测损失，以通过强调本地上下文的情感信息来增强模型。三个常见数据集的实验结果表明，局部上下文感知网络在提取本地上下文特征时的表现优于现有方法。此外，本地上下文感知的框架很容易适应许多模型，并有可能改善其他目标级任务。
Target-oriented sentiment classification is a fine-grained task of natural language processing to analyze the sentiment polarity of the targets. To improve the performance of sentiment classification, many approaches proposed various attention mechanisms to capture the important context words of a target. However, previous approaches ignored the significant relatedness of a target's sentiment and its local context. This paper proposes a local context-aware network (LCA-Net), equipped with the local context embedding and local context prediction loss, to strengthen the model by emphasizing the sentiment information of the local context. The experimental results on three common datasets show that local context-aware network performs superior to existing approaches in extracting local context features. Besides, the local context-aware framework is easy to adapt to many models, with the potential to improve other target-level tasks.
