Learning Language in Logic, 1925. izdevums

Pirmais vāks
James 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.

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Atlasītās lappuses

Saturs

An Introduction to Inductive Logic Programming and Learning Language in Logic
5
A Brief Introduction to Natural Language Processing for Nonlinguists
38
A Closer Look at the Automatic Induction of Linguistic Knowledge
51
Scaling Up without Dumbing Down
59
Learning to Lemmatise Slovene Words
71
Achievements and Prospects of Learning Word Morphology with Inductive Logic Programming
91
Learning the Logic of Simple Phonotactics
112
Grammar Induction as Substructural Inductive Logic Programming
129
Iterative PartofSpeech Tagging
172
DCG Induction Using MDL and Parsed Corpora
186
Learning LogLinear Models on ConstraintBased Grammars for Disambiguation
201
Unsupervised Lexical Learning with Categorial Grammars Using the LLL Corpus
220
Induction of Recursive Transfer Rules
239
Learning for Text Categorization and Information Extraction with ILP
249
CorpusBased Learning of Semantic Relations by the ILP System Asium
261
Improving Learning by Choosing Examples Intelligently in Two Natural Language Tasks
281

Experiments in Inductive Chart Parsing
145
ILP in PartofSpeech Tagging An Overview
159
Author Index
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