Multilingual Subjectivity: Are More Languages Better?

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This paper discusses multilingual subjectivity.

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9 p.

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Banea, Carmen; Mihalcea, Rada, 1974- & Wiebe, Janyce M. August 2010.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 169 times . More information about this paper can be viewed below.

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This paper discusses multilingual subjectivity.

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9 p.

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Abstract: While subjectivity related research in other languages has increased, most of the work focuses on single languages. This paper explores the integration of features originating from multiple languages into a machine learning approach to subjectivity analysis, and aims to show that this enriched feature set provides for more effective modeling for the source as well as the target languages. We show not only that they are able to achieve over 75% macro accuracy in all of the six languages they experiment with, but also that by using features drawn from multiple languages they can construct high-precision meta-classifiers with a precision of over 83%.

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  • International Conference on Computational Linguistics (COLING), 2010, Beijing, China

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UNT Scholarly Works

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  • August 2010

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  • Jan. 31, 2011, 2:01 p.m.

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  • March 27, 2014, 12:27 p.m.

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Banea, Carmen; Mihalcea, Rada, 1974- & Wiebe, Janyce M. Multilingual Subjectivity: Are More Languages Better?, paper, August 2010; (digital.library.unt.edu/ark:/67531/metadc31025/: accessed October 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.