Get our free extension to see links to code for papers anywhere online!Free add-on: code for papers everywhere!Free add-on: See code for papers anywhere!
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.
* M\'etais E., Meziane F., Horacek H., Cimiano P. (eds) Natural
Language Processing and Information Systems. NLDB 2020. Lecture Notes in
Computer Science, vol 12089. Springer, Cham * 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