Natural Language Processing techniques applied to speech technologies. Trilla, A. Ph.D. Thesis, Universitat Ramon Llull.
Natural Language Processing techniques applied to speech technologies [pdf]Paper  abstract   bibtex   
This dissertation relates a first research stage in the pursuit of the Ph.D. degree in "Information and communication Technologies and its Management" at La Salle  Universitat Ramon Llull. The dissertation presents an overview of the various approaches to Text- based Sentiment Prediction in order to reveal their effectiveness in the tripartite sentiment recognition task, i.e., the identification of positive, negative and neutral orientations in text. It discusses the application of the bases that support these diverse proposals, from the feature extraction stage to the classification phase, contrasting the contributions that each method yields through a set of experiments with the Semeval 2007 dataset and the Fifty Word Fiction corpus.
@phdthesis{trilla_natural_2010,
	Author = {Trilla, Alexandre},
	Date = {2010},
	Date-Modified = {2016-09-23 19:24:14 +0000},
	File = {Attachment:files/11180/Trilla - 2010 - Natural Language Processing techniques applied to speech technologies.pdf:application/pdf},
	Keywords = {emotions, language technology, linguistic processing, natural language processing, speaking styles, speech synthesis, speech technology, text pre-processing, text-to-speech},
	School = {Universitat Ramon Llull},
	Title = {Natural Language Processing techniques applied to speech technologies},
	Url = {http://atrilla.net/data/files/dea_atrilla.pdf},
	Abstract = {This dissertation relates a first research stage in the pursuit of the Ph.D. degree in "Information and communication Technologies and its Management" at La Salle  Universitat Ramon Llull. The dissertation presents an overview of the various approaches to Text- based Sentiment Prediction in order to reveal their effectiveness in the tripartite sentiment recognition task, i.e., the identification of positive, negative and neutral orientations in text. It discusses the application of the bases that support these diverse proposals, from the feature extraction stage to the classification phase, contrasting the contributions that each method yields through a set of experiments with the Semeval 2007 dataset and the Fifty Word Fiction corpus.},
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