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Titlebook: Learning Language in Logic; James Cussens,Sa?o D?eroski Book 2000 Springer-Verlag Berlin Heidelberg 2000 MDL.Mathematica.Natural.Processin

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書目名稱Learning Language in Logic
編輯James Cussens,Sa?o D?eroski
視頻videohttp://file.papertrans.cn/583/582760/582760.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Learning Language in Logic;  James Cussens,Sa?o D?eroski Book 2000 Springer-Verlag Berlin Heidelberg 2000 MDL.Mathematica.Natural.Processin
描述This volume has its origins in the ?rst Learning Language in Logic (LLL) wo- shop which took place on 30 June 1999 in Bled, Slovenia immediately after the Ninth International Workshop on Inductive Logic Programming (ILP’99) and the Sixteenth International Conference on Machine Learning (ICML’99). LLL is a research area lying at the intersection of computational linguistics, machine learning, and computational logic. As such it is of interest to all those working in these three ?elds. I am pleased to say that the workshop attracted subm- sions from both the natural language processing (NLP) community and the ILP community, re?ecting the essentially multi-disciplinary nature of LLL. Eric Brill and Ray Mooney were invited speakers at the workshop and their contributions to this volume re?ect the topics of their stimulating invited talks. After the workshop authors were given the opportunity to improve their papers, the results of which are contained here. However, this volume also includes a substantial amount of two sorts of additional material. Firstly, since our central aim is to introduce LLL work to the widest possible audience, two introductory chapters have been written. Dzeros
出版日期Book 2000
關(guān)鍵詞MDL; Mathematica; Natural; Processing; Syntax; constraint; grammar; information extraction; knowledge; learni
版次1
doihttps://doi.org/10.1007/3-540-40030-3
isbn_softcover978-3-540-41145-1
isbn_ebook978-3-540-40030-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2000
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An Introduction to Inductive Logic Programming and Learning Language in Logiclution, least general generalisation, relative least general generalisation, inverse entailment, saturation, refinement and abduction. We conclude with an overview of this volume and pointers to future work.
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Iterative Part-of-Speech Tagging underneath. Here we consider one particular relational learning algorithm, CSC(RC), and we induce first order theories from positive examples and background knowledge that are able to successfully tag a relatively large corpus in Portuguese.
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