Studying Attention Models in Sentiment Attitude Extraction Task

06/20/2020
by   Nicolay Rusnachenko, et al.
0

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 weight distributions in dependence on the term type.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

06/23/2020

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

In the sentiment attitude extraction task, the aim is to identify <<atti...
08/27/2018

Extracting Sentiment Attitudes From Analytical Texts

In this paper we present the RuSentRel corpus including analytical texts...
08/25/2020

Simple Unsupervised Similarity-Based Aspect Extraction

In the context of sentiment analysis, there has been growing interest in...
06/19/2020

Sentiment Frames for Attitude Extraction in Russian

Texts can convey several types of inter-related information concerning o...
10/15/2020

Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow f...
07/05/2021

Sarcasm Detection: A Comparative Study

Sarcasm detection is the task of identifying irony containing utterances...
08/19/2019

Fine-grained Sentiment Analysis with Faithful Attention

While the general task of textual sentiment classification has been wide...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.