Learning Language in Logic, 1925. izdevumsJames Cussens, Saso Dzeroski Springer Science & Business Media, 2000. gada 27. sept. - 299 lappuses 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. Dzeroski, ? Cussens and Manandhar provide an - troduction to ILP and LLL and Thompson provides an introduction to NLP. |
Saturs
An Introduction to Inductive Logic Programming and Learning Language in Logic | 3 |
A Brief Introduction to Natural Language Processing for Nonlinguists | 36 |
A Closer Look at the Automatic Induction of Linguistic Knowledge | 49 |
Scaling Up without Dumbing Down | 57 |
Learning to Lemmatise Slovene Words | 69 |
Achievements and Prospects of Learning Word Morphology with Inductive Logic Programming | 89 |
Learning the Logic of Simple Phonotactics | 110 |
Grammar Induction as Substructural Inductive Logic Programming | 127 |
Iterative PartofSpeech Tagging | 170 |
DCG Induction Using MDL and Parsed Corpora | 184 |
Learning LogLinear Models on ConstraintBased Grammars for Disambiguation | 199 |
Unsupervised Lexical Learning with Categorial Grammars Using the LLL Corpus | 218 |
Induction of Recursive Transfer Rules | 234 |
Learning for Text Categorization and Information Extraction with ILP | 247 |
CorpusBased Learning of Semantic Relations by the ILP System Asium | 259 |
Improving Learning by Choosing Examples Intelligently in Two Natural Language Tasks | 279 |
Experiments in Inductive Chart Parsing | 143 |
ILP in PartofSpeech Tagging An Overview | 157 |
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Learning Language in Logic, 1925. izdevums James Cussens,Saso Dzeroski Ierobežota priekšskatīšana - 2000 |
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abduction accuracy active learning adjectives ambiguity analysis annotation application approach Artificial Intelligence Asium background knowledge basic word clause Califf Categorial Grammar chart chart parser complex Computational Linguistics constraints context corpus Cussens decision list defined derive disambiguation Dzeroski Eineborg encoded Erjavec evaluation experiments given grammar rules grammatical relations hypothesis ILP systems Inductive Logic Programming inference information extraction input iterative learner learning algorithm lemma lemmatisation lexical lexicon log-linear models machine learning Manandhar methods Mooney morphemes morphology MSDs Muggleton Natural Language Processing negative examples noun phrase P-Progol parser part-of-speech tagging performance positive examples predicate prefix probabilistic problem Progol Prolog properties queries Rapier recursive representation segmentation selection semantic sentence sequence Slovene statistical structure subcategorization frame substructural logic suffix clause syntactic Table tagger target task techniques theory training data training examples training set Treebank unknown words unsupervised unsupervised learning variables verb vp(S word form