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In the sentiment attitude extraction task, the aim is to identify<<attitudes>> -- sentiment relations between entities mentioned in text. Inthis paper, we provide a study on attention-based context encoders in thesentiment attitude extraction task. For this task, we adapt attentive contextencoders of two types: (1) feature-based; (2) self-based. In our study, weutilize the corpus of Russian analytical texts RuSentRel and automaticallyconstructed news collection RuAttitudes for enriching the training set. Weconsider the problem of attitude extraction as two-class (positive, negative)and three-class (positive, negative, neutral) classification tasks for wholedocuments. Our experiments with the RuSentRel corpus show that the three-classclassification models, which employ the RuAttitudes corpus for training, resultin 10% increase and extra 3% by F1, when model architectures include theattention mechanism. We also provide the analysis of attention weightdistributions in dependence on the term type.

Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision