Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision
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: (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 experiments 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.
Cite
@article{arxiv.2006.13730,
title = {Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision},
author = {Nicolay Rusnachenko and Natalia Loukachevitch},
journal= {arXiv preprint arXiv:2006.13730},
year = {2020}
}
Comments
10 pages, 9 figures. The preprint of an article published in the proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics (WIMS 2020). The final authenticated publication is available online at https://doi.org/10.1145/3405962.3405985. arXiv admin note: substantial text overlap with arXiv:2006.11605