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  • Entropy guided transformation learning [Elektronski vir] : algorithms and applications
    Santos, Cícero Nogueira
    Entropy Guided Transformation Learning: Algorithms and Applications (ETL) presents a machine learning algorithm for classification tasks. ETL generalizes Transformation Based Learning (TBL) by ... solving the TBL bottleneck: the construction of good template sets. ETL automatically generates templates using Decision Tree decomposition.The authors describe ETL Committee, an ensemble method that uses ETL as the base learner. Experimental results show that ETL Committee improves the effectiveness of ETL classifiers. The application of ETL is presented to four Natural Language Processing (NLP) tasks: part-of-speech tagging, phrase chunking, named entity recognition and semantic role labeling. Extensive experimental results demonstrate that ETL is an effective way to learn accurate transformation rules, and shows better results than TBL with handcrafted templates for the four tasks. By avoiding the use of handcrafted templates, ETL enables the use of transformation rules to a greater range of tasks.Suitable for both advanced undergraduate and graduate courses, Entropy Guided Transformation Learning: Algorithms and Applications provides a comprehensive introduction to ETL and its NLP applications
    Vrsta gradiva - e-knjiga
    Založništvo in izdelava - London ; New York : Springer, cop. 2012
    Jezik - angleški
    ISBN - 978-1-4471-2978-3; 1-4471-2978-4; 1-4471-2977-6; 978-1-4471-2977-6
    COBISS.SI-ID - 1544933343

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    SpringerLink e-books 2008-2012

    Celotno besedilo dostopno za uporabnike SpringerLink slovenskega konzorcija neprofitnih institucij

    Full text accessible to the users of SpringerLink Slovenian Consortium of Non-Profit Institutions


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