Studying Attention Models in Sentiment Attitude Extraction Task

Nicolay Rusnachenko, Natalia V. Loukachevitch

Abstract: 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 ( https://github.com/nicolay-r/attitude-extraction-with-attention ) 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.