Attention-Based Neural Networks for Sentiment Attitude Extraction Using Distant Supervision

Rusnachenko, N.; Loukachevitch, N.

Авторы: Rusnachenko Nicolay, Loukachevitch Natalia
Сборник: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics WIMS 2020
Год издания: 2020
Издательство: Association for Computing Machinery
Местоположение издательства: New York, N.Y., United States
Первая страница: 159
Последняя страница: 168
DOI: 10.1145/3405962.3405985
Аннотация: 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: (1) feature-based; (2) self-based. In our study, we utilize the corpus of Russian analytical texts RuSentRel and automatically constructed news collection RuAttitudes for enriching the training set. We consider the problem of attitude extraction as two-class (positive, negative) and three-class (positive, negative, neutral) classification tasks for whole documents. Our experiments1 with the RuSentRel corpus show that the three-class classification models, which employ the RuAttitudes corpus for training, result in 10% increase and extra 3% by F1, when model architectures include the attention mechanism. We also provide the analysis of attention weight distributions in dependence on the term type.
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Attention-Based Neural Networks for Sentiment Attitude Extraction Using Distant Supervisionстатья

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