Learning Language in Logic

Learning Language in Logic edited by James Cussens, Saso Dzeroski.

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). LL...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Cussens, James (Editor), Dzeroski, Saso (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.
Edition:1st ed. 2000.
Series:Lecture Notes in Artificial Intelligence ; 1925
Springer eBook Collection.
Subjects:
Artificial intelligence.
Mathematical logic.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Introductions & 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.

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