Does Coreference Resolution Improve Aspect Based Sentiment Analysis?
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 ta...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/61818 |
| Acceso en línea: | http://hdl.handle.net/10810/61818 |
| Access Level: | acceso abierto |
| Palabra clave: | aspect-based sentiment analysis coreference resolution opinion target extraction aspect category detection |
| Sumario: | 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 |
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