论文标题
基于注意的神经网络，用于使用遥远监督的情感态度提取
Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision
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：（1）基于功能； （2）基于自我。在我们的研究中，我们利用了俄罗斯分析文本的语料库Rusentrel，并自动构建了新闻集的鲁蒂特，以丰富培训集。我们将态度提取的问题视为整个文档的两级（正，负）和三类（正，负，中性）分类任务。我们对Rusentrel语料库进行的实验表明，当模型体系结构包括注意机制时，使用Ruattududududududududududududududududude corpus进行了训练的三类分类模型会增加10％，而F1则增加了3％。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: (1) feature-based; (2) self-based. In our study, we utilize the corpus of Russian analytical texts RuSentRel and automatically constructed news collection RuAttitudes for enriching the training set. We consider the problem of attitude extraction as two-class (positive, negative) and three-class (positive, negative, neutral) classification tasks for whole documents. Our experiments with the RuSentRel corpus show that the three-class classification models, which employ the RuAttitudes corpus for training, result in 10% increase and extra 3% by F1, when model architectures include the attention mechanism. We also provide the analysis of attention weight distributions in dependence on the term type.
