Improving Subjectivity Detection using Unsupervised Subjectivity Word Sense Disambiguation
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Autores: Reynier Ortega, Adrian Fonseca, Yoan Gutiérrez Vázquez
, Andrés Montoyo Guijarro
- Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 51, 2013, págs. 179-186
- Idioma: inglés
- Enlaces
- Dialnet Métricas: 1 Cita
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Resumen
- español
En este trabajo se presenta un m´etodo para la detecci´on de subjetividad a nivel de oraciones basado en la desambiguaci´on subjetiva del sentido de las palabras. Para ello se extiende un m´etodo de desambiguaci´on sem´antica basado en agrupamiento de sentidos para determinar cu´ando las palabras dentro de la oraci´on est´an siendo utilizadas de forma subjetiva u objetiva. En nuestra propuesta se utilizan recursos sem´anticos anotados con valores de polaridad y emociones para determinar cu´ando un sentido de una palabra puede ser considerado subjetivo u objetivo.
Se presenta un estudio experimental sobre la detecci´on de subjetividad en oraciones, en el cual se consideran las colecciones del corpus MPQA y Movie Review Dataset, as´ý como los recursos sem´anticos SentiWordNet, Micro-WNOp y WordNet-Affect.
Los resultados obtenidos muestran que nuestra propuesta contribuye de manera significativa en la detecci´on de subjetividad
- English
In this work, we present a sentence-level subjectivity detection method, which relies on Subjectivity Word Sense Disambiguation (SWSD). We use an unsupervised sense clustering-based method for SWSD. In our method, semantic resources tagged with emotions and sentiment polarities are used to apply subjectivity detection, intervening Word Sense Disambiguation sub-tasks. Through an experimental study, we empirically validated the proposed method over two subjectivity collections, MPQA Corpus and Movie Review Dataset, using three widely popular opinion-mining resources SentiWordNet, WordNet-Affect and Micro-WNOp. The results show that our proposal performs significantly better than our proposed baseline.
- español
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