Studying Attention Models in Sentiment Attitude Extraction Task

Code

• github.com/nicolay-r/attitude-extraction-with-attention
  OfficialIn papernone★ 0
• github.com/nicolay-r/AREkit
  tf★ 65
• github.com/nicolay-r/RuSentRel-Leaderboard
  none★ 8
• github.com/nicolay-r/mlp-attention
  tf★ 3

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.

Reproductions