Titlebook: Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing; Stefan Wermter,Ellen Riloff,Gabriele Schel

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0302-9743 the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.978-3-540-60925-4978-3-540-49738-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Turbulent Shear Layers in Supersonic Flowl class. This method does not depend on any specific grammar or set of semantical categories, so it can be used on (almost) any existing system. We present experimental results that show our method gives a considerable improvement over regular stochastic grammars.

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https://doi.org/10.1007/3-540-33591-9he accuracy of the statistical method remains 10% below the performance of human experts. This suggests a limit on what can be learned automatically from text, and points to the need to combine machine learning with human expertise.

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Lecture Notes in Computer Sciencel natural language processing. We report experimental results of applying a specific type of committee-based selection during training of a stochastic part-of-speech tagger, and demonstrate substantially improved learning rates over complete training using all of the text.

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Separating learning and representation,ed the potential to correctly recognise embeddings of any length. These findings illustrate the benefits of the study of representation, which can provide a basis for the development of novel learning rules.

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