The Utility of Knowledge in Inductive Learning
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Forgetting Exceptions is Harmful in Language Learning
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Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Information Retrieval
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A memory-based approach to learning shallow natural language patterns
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Chunking with maximum entropy models
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Text chunking by system combination
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Learning computational grammars
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
Introduction to special issue on machine learning approaches to shallow parsing
The Journal of Machine Learning Research
Mining information extraction rules from datasheets without linguistic parsing
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Hierarchical rule generalisation for speaker identification in fiction books
SAICSIT '06 Proceedings of the 2006 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Proceedings of the third international workshop on Data and text mining in bioinformatics
Coping with Distribution Change in the Same Domain Using Similarity-Based Instance Weighting
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
A framework for schema-driven relationship discovery from unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Automatic partial parsing rule acquisition using decision tree induction
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
International Journal of Data Mining and Bioinformatics
Ripple-down rules with censored production rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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We present in this article a top-down inductive system, ALLiS, for learning linguistic structures. Two difficulties came up during the development of the system: the presence of a significant amount of noise in the data and the presence of exceptions linguistically motivated. It is then a challenge for an inductive system to learn rules from this kind of data. This leads us to add a specific mechanism, refinement, which enables learning rules and their exceptions. In the first part of this article we evaluate the usefulness of this device and show that it improves results when learning linguistic structures.In the second part, we explore how to improve the efficiency of the system by using prior knowledge. Since Natural Language is a strongly structured object, it may be important to investigate whether linguistic knowledge can help to make natural language learning more efficiently and accurately. This article presents some experiments demonstrating that linguistic knowledge improves learning. The system has been applied to the shared task of the CoNLL'00 workshop.