Studying attention models in sentiment attitude extraction task

Authors: Rusnachenko, N. and Loukachevitch, N.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 12089 LNCS

Pages: 157-169

eISSN: 1611-3349

ISBN: 9783030513092

ISSN: 0302-9743

DOI: 10.1007/978-3-030-51310-8_15

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 (https://github.com/nicolay-r/attitude-extraction-with-attention) 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.

Source: Scopus