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arXiv · 2006.11605

Studying Attention Models in Sentiment Attitude Extraction Task

Nicolay Rusnachenko·Natalia Loukachevitch
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Abstract

In the sentiment attitude extraction task, the aim is to identify < > -- 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.

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Nicolay Rusnachenko, Natalia Loukachevitch. 2020-06-20. Studying Attention Models in Sentiment Attitude Extraction Task. https://doi.org/10.1007/978-3-030-51310-8_15

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