EmotiBlog: a finer-grained and more precise learning of subjectivity expression models
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Título: | EmotiBlog: a finer-grained and more precise learning of subjectivity expression models |
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Autor/es: | Boldrini, Ester | Balahur Dobrescu, Alexandra | Martínez-Barco, Patricio | Montoyo, Andres |
Grupo/s de investigación o GITE: | Procesamiento del Lenguaje y Sistemas de Información (GPLSI) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | EmotiBlog | Precise learning | Subjectivity expression models |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 2010 |
Editor: | Association for Computational Linguistics (ACL) |
Cita bibliográfica: | BOLDRINI, Ester, et al. "EmotiBlog: a finer-grained and more precise learning of subjectivity expression models". En: Proceedings of the Fourth Linguistic Annotation Workshop, ACL 2010 : Uppsala, Sweden, 15-16 July 2010. Stroudsburg, PA : ACL, 2010. ISBN 978-1-932432-72-5, pp. 1-10 |
Resumen: | The exponential growth of the subjective information in the framework of the Web 2.0 has led to the need to create Natural Language Processing tools able to analyse and process such data for multiple practical applications. They require training on specifically annotated corpora, whose level of detail must be fine enough to capture the phenomena involved. This paper presents EmotiBlog – a fine-grained annotation scheme for subjectivity. We show the manner in which it is built and demonstrate the benefits it brings to the systems using it for training, through the experiments we carried out on opinion mining and emotion detection. We employ corpora of different textual genres –a set of annotated reported speech extracted from news articles, the set of news titles annotated with polarity and emotion from the SemEval 2007 (Task 14) and ISEAR, a corpus of real-life self-expressed emotion. We also show how the model built from the EmotiBlog annotations can be enhanced with external resources. The results demonstrate that EmotiBlog, through its structure and annotation paradigm, offers high quality training data for systems dealing both with opinion mining, as well as emotion detection. |
Patrocinador/es: | This paper has been supported by Ministerio de Ciencia e Innovación- Spanish Government (grant no. TIN2009-13391-C04-01), and Conselleria d'Educación-Generalitat Valenciana (grant no. PRO-METEO/2009/119 and A-COMP/2010/288). |
URI: | http://hdl.handle.net/10045/22494 |
ISBN: | 978-1-932432-72-5 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/conferenceObject |
Revisión científica: | si |
Aparece en las colecciones: | INV - GPLSI - Comunicaciones a Congresos, Conferencias, etc. |
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2010_Boldrini_LAW.pdf | 448,55 kB | Adobe PDF | Abrir Vista previa | |
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