Learning Language in Logic [electronic resource] /
Cussens, James.editor.
Dzeroski, Saso.editor.
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.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references at the end of each chapters and index.
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.
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.
Artificial intelligence.
Mathematical logic.
Artificial Intelligence.
Mathematical Logic and Formal Languages.