Studying Attention Models in Sentiment Attitude Extraction Task

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Authors Nicolay Rusnachenko, Natalia Loukachevitch
Journal/Conference Name Natural Language Processing and Information Systems
Paper Category
Paper 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 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.
Date of publication 2020
Code Programming Language Python
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