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020 _a9783540400301
_9978-3-540-40030-1
024 7 _a10.1007/3-540-40030-3
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
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072 7 _aCOM004000
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_223
245 1 0 _aLearning Language in Logic
_h[electronic resource] /
_cedited by James Cussens, Sašo Džeroski.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2000.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2000.
300 _aX, 306 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v1925
505 0 _aIntroductions & Overviews -- An Introduction to Inductive Logic Programming and Learning Language in Logic -- A Brief Introduction to Natural Language Processing for Non-linguists -- A Closer Look at the Automatic Induction of Linguistic Knowledge -- Learning for Semantic Interpretation: Scaling Up without Dumbing Down -- Morphology & Phonology -- Learning to Lemmatise Slovene Words -- Achievements and Prospects of Learning Word Morphology with Inductive Logic Programming -- Learning the Logic of Simple Phonotactics -- Syntax -- Grammar Induction as Substructural Inductive Logic Programming -- Experiments in Inductive Chart Parsing -- ILP in Part-of-Speech Tagging — An Overview -- Iterative Part-of-Speech Tagging -- DCG Induction Using MDL and Parsed Corpora -- Learning Log-Linear Models on Constraint-Based Grammars for Disambiguation -- Unsupervised Lexical Learning with Categorial Grammars Using the LLL Corpus -- Induction of Recursive Transfer Rules -- Learning for Text Categorization and Information Extraction with ILP -- Corpus-Based Learning of Semantic Relations by the ILP System, Asium -- Improving Learning by Choosing Examples Intelligently in Two Natural Language Tasks.
520 _aThis 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. Dzeroski, ? Cussens and Manandhar provide an - troduction to ILP and LLL and Thompson provides an introduction to NLP.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aMathematical Logic and Formal Languages.
700 1 _aCussens, James.
_eeditor.
700 1 _aDžeroski, Sašo.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540411451
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v1925
856 4 0 _uhttp://dx.doi.org/10.1007/3-540-40030-3
942 _2EBK4807
_cEBK
999 _c34101
_d34101