论文标题
在情感态度提取任务中研究注意力模型
Studying Attention Models in Sentiment Attitude Extraction Task
论文作者
论文摘要
在情感态度提取任务中，目的是确定<<态度>> - 文本中提到的实体之间的情感关系。在本文中，我们在情感态度提取任务中对基于注意力的上下文编码者进行了研究。对于此任务，我们调整了两种类型的细心上下文编码：（i）基于功能的； （ii）基于自我。我们对俄罗斯分析文本语料库的实验Rusentrel表明，经过细心编码的模型的表现优于未经它们的训练，并在F1上增加了1.5-5.9％。我们还提供了注意力分布的分析，以依赖于术语类型。
In the sentiment attitude extraction task, the aim is to identify <<attitudes>> -- sentiment relations between entities mentioned in text. In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task. For this task, we adapt attentive context encoders of two types: (i) feature-based; (ii) self-based. Our experiments with a corpus of Russian analytical texts RuSentRel illustrate that the models trained with attentive encoders outperform ones that were trained without them and achieve 1.5-5.9% increase by F1. We also provide the analysis of attention weight distributions in dependence on the term type.
