dc.contributor.advisor | Agerri Gascón, Rodrigo | |
dc.contributor.author | Ryhänen, Rosa-Maria Kristiina | |
dc.date.accessioned | 2023-06-30T14:48:14Z | |
dc.date.available | 2023-06-30T14:48:14Z | |
dc.date.issued | 2023-06-30 | |
dc.identifier.uri | http://hdl.handle.net/10810/61818 | |
dc.description.abstract | Aspect-Based Sentiment Analysis (ABSA) has generally focused on extracting explicit opinion targets and classifying them into polarities and categories. Most approaches ignore implicitly expressed opinions, even though they make up a significant part of language; in fact, approximately 25% of the targets in the SemEval ABSA 2016 English restaurant reviews (Pontiki et al., 2016) are implicit and are not taken into consideration when training a model. We propose to solve a part of the implicit targets with coreference resolution in order to improve two ABSA tasks: opinion target extraction and aspect category detection. Our results suggest that coreference resolution helps to perform opinion target extraction and aspect category detection, when the latter is handled as a multi-label classification task. The data and code are publicly available on GitHub
https://github.com/rosamariaryh/absa-coref | es_ES |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | aspect-based sentiment analysis | |
dc.subject | coreference resolution | |
dc.subject | opinion target extraction | |
dc.subject | aspect category detection | |
dc.title | Does Coreference Resolution Improve Aspect Based Sentiment Analysis? | es_ES |
dc.type | info:eu-repo/semantics/masterThesis | |
dc.date.updated | 2022-06-13T10:39:34Z | |
dc.language.rfc3066 | es | |
dc.rights.holder | © 2022, la autora | |
dc.contributor.degree | Máster Universitario en Análisis y Procesamiento del Lenguaje | |
dc.contributor.degree | Hizkuntzaren Azterketa eta Prozesamendua Unibertsitate Masterra | |
dc.identifier.gaurregister | 123242-1020427-09 | es_ES |
dc.identifier.gaurassign | 137892-1020427 | es_ES |