Cross-domain polarity classification using a knowledge-enhanced meta-classifier
In this paper, we propose the use of meta-learning to combine and enrich those approaches by adding also other knowledge-based features. In addition to the aforementioned classical approaches, our system uses the BabelNet multilingual semantic network to generate features derived from word sense dis...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/63907 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/63907 |
| Access Level: | acceso abierto |
| Palavra-chave: | Sentiment analysis Cross-domain polarity classification Meta-learning Word sense disambiguation Semantic network LENGUAJES Y SISTEMAS INFORMATICOS |
| Resumo: | In this paper, we propose the use of meta-learning to combine and enrich those approaches by adding also other knowledge-based features. In addition to the aforementioned classical approaches, our system uses the BabelNet multilingual semantic network to generate features derived from word sense disambiguation and vocabulary expansion. Experimental results show state-of-theart performance on single and cross-domain polarity classification. |
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