The Experts below are selected from a list of 5046 Experts worldwide ranked by ideXlab platform

Roman Kern - One of the best experts on this subject based on the ideXlab platform.

Andi Rexha - One of the best experts on this subject based on the ideXlab platform.

Paolo Rosso - One of the best experts on this subject based on the ideXlab platform.

  • string kernels for Polarity Classification a study across different languages
    Applications of Natural Language to Data Bases, 2018
    Co-Authors: Rosa M Gimenezperez, Marc Francosalvador, Paolo Rosso
    Abstract:

    The Polarity Classification task has as objective to automatically deciding whether a subjective text is positive or negative. Using a cross-domain setting implies the use of different domains for the training and testing. Recently, string kernels, a method which does not employ domain adaptation techniques has been proposed. In this work, we analyse the performance of this method across four different languages: English, German, French and Japanese. Experimental results show the strong potential of this approach independently from the language.

  • NLDB - String Kernels for Polarity Classification: A Study Across Different Languages
    Natural Language Processing and Information Systems, 2018
    Co-Authors: Rosa M. Giménez-pérez, Marc Franco-salvador, Paolo Rosso
    Abstract:

    The Polarity Classification task has as objective to automatically deciding whether a subjective text is positive or negative. Using a cross-domain setting implies the use of different domains for the training and testing. Recently, string kernels, a method which does not employ domain adaptation techniques has been proposed. In this work, we analyse the performance of this method across four different languages: English, German, French and Japanese. Experimental results show the strong potential of this approach independently from the language.

  • single and cross domain Polarity Classification using string kernels
    Conference of the European Chapter of the Association for Computational Linguistics, 2017
    Co-Authors: Rosa M Gimenezperez, Marc Francosalvador, Paolo Rosso
    Abstract:

    The Polarity Classification task aims at automatically identifying whether a subjective text is positive or negative. When the target domain is different from those where a model was trained, we refer to a cross-domain setting. That setting usually implies the use of a domain adaptation method. In this work, we study the single and cross-domain Polarity Classification tasks from the string kernels perspective. Contrary to classical domain adaptation methods, which employ texts from both domains to detect pivot features, we do not use the target domain for training. Our approach detects the lexical peculiarities that characterise the text Polarity and maps them into a domain independent space by means of kernel discriminant analysis. Experimental results show state-of-the-art performance in single and cross-domain Polarity Classification.

  • EACL (2) - Single and Cross-domain Polarity Classification using String Kernels
    Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2 Short Papers, 2017
    Co-Authors: Rosa M. Giménez-pérez, Marc Franco-salvador, Paolo Rosso
    Abstract:

    The Polarity Classification task aims at automatically identifying whether a subjective text is positive or negative. When the target domain is different from those where a model was trained, we refer to a cross-domain setting. That setting usually implies the use of a domain adaptation method. In this work, we study the single and cross-domain Polarity Classification tasks from the string kernels perspective. Contrary to classical domain adaptation methods, which employ texts from both domains to detect pivot features, we do not use the target domain for training. Our approach detects the lexical peculiarities that characterise the text Polarity and maps them into a domain independent space by means of kernel discriminant analysis. Experimental results show state-of-the-art performance in single and cross-domain Polarity Classification.

  • Cross-domain Polarity Classification using a knowledge-enhanced meta-classifier
    Knowledge-Based Systems, 2015
    Co-Authors: Marc Franco-salvador, Fermín L. Cruz, José A. Troyano, Paolo Rosso
    Abstract:

    We propose a new generic meta-learning-based approach to Polarity categorization.Study impact of word sense disambiguation and vocabulary expansion-based features.State-of-the-art results on single and cross-domain Polarity categorization.Our approach does not perform any domain adaptation, therefore it is generic.Our approach obtains the most stable results across the different tested domains. Current approaches to single and cross-domain Polarity Classification usually use bag of words, n-grams or lexical resource-based classifiers. 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-the-art performance on single and cross-domain Polarity Classification. Contrary to other approaches, ours is generic. These results were obtained without any domain adaptation technique. Moreover, the use of meta-learning allows our approach to obtain the most stable results across domains. Finally, our empirical analysis provides interesting insights on the use of semantic network-based features.

Mauro Dragoni - One of the best experts on this subject based on the ideXlab platform.

Mark Kroll - One of the best experts on this subject based on the ideXlab platform.