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Studying Attention Models in Sentiment Attitude Extraction Task
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298196/ http://dx.doi.org/10.1007/978-3-030-51310-8_15 |
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author | Rusnachenko, Nicolay Loukachevitch, Natalia |
author_facet | Rusnachenko, Nicolay Loukachevitch, Natalia |
author_sort | Rusnachenko, Nicolay |
collection | PubMed |
description | 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 [Formula: see text]. We also provide the analysis of attention weight distributions in dependence on the term type. |
format | Online Article Text |
id | pubmed-7298196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72981962020-06-17 Studying Attention Models in Sentiment Attitude Extraction Task Rusnachenko, Nicolay Loukachevitch, Natalia Natural Language Processing and Information Systems Article 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 [Formula: see text]. We also provide the analysis of attention weight distributions in dependence on the term type. 2020-05-26 /pmc/articles/PMC7298196/ http://dx.doi.org/10.1007/978-3-030-51310-8_15 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Rusnachenko, Nicolay Loukachevitch, Natalia Studying Attention Models in Sentiment Attitude Extraction Task |
title | Studying Attention Models in Sentiment Attitude Extraction Task |
title_full | Studying Attention Models in Sentiment Attitude Extraction Task |
title_fullStr | Studying Attention Models in Sentiment Attitude Extraction Task |
title_full_unstemmed | Studying Attention Models in Sentiment Attitude Extraction Task |
title_short | Studying Attention Models in Sentiment Attitude Extraction Task |
title_sort | studying attention models in sentiment attitude extraction task |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298196/ http://dx.doi.org/10.1007/978-3-030-51310-8_15 |
work_keys_str_mv | AT rusnachenkonicolay studyingattentionmodelsinsentimentattitudeextractiontask AT loukachevitchnatalia studyingattentionmodelsinsentimentattitudeextractiontask |