Studying Attention Models in Sentiment Attitude Extraction Task
arXiv:2006.11605 · doi:10.1007/978-3-030-51310-8_15
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.
This is a preprint of an article published in the Proceedings of the 25th International Conference on Natural Language and Information Systems. The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-51310-8_15