C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Real-time automatic insertion of accents in French text
Natural Language Engineering
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
On Combining Classifier Mass Functions for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Letter level learning for language independent diacritics restoration
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A UNIFIED APPROACH TO GRAPHEME-TO-PHONEME CONVERSION FOR THE PLATTOS SLOVENIAN TEXT-TO-SPEECH SYSTEM
Applied Artificial Intelligence
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We tested the ability of humans and machines (data mining techniques) to assign stress to Slovene words. This is a challenging comparison for machines since humans accomplish the task outstandingly even on unknown words without any context. The goal of finding good machine-made models for stress assignment was set by applying new methods and by making use of a known theory about rules for stress assignment in Slovene. The upgraded data mining methods outperformed expert-defined rules on practically all subtasks, thus showing that data mining can more than compete with humans when constructing formal knowledge about stress assignment is concerned. Unfortunately, compared to humans directly, the data mining methods still failed to achieve as good results as humans on assigning stress to unknown words.