C4.5: programs for machine learning
C4.5: programs for machine learning
IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms
Artificial Intelligence Review - Special issue on lazy learning
Statistical methods for speech recognition
Statistical methods for speech recognition
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Large scale experiments on correction of confused words
ACSC '01 Proceedings of the 24th Australasian conference on Computer science
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A classification approach to word prediction
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Memory-based learning: using similarity for smoothing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Memory-based text correction for preposition and determiner errors
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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We present a classification-based word prediction model based on IGTree, a decision-tree induction algorithm with favorable scaling abilities and a functional equivalence to n-gram models with back-off smoothing. Through a first series of experiments, in which we train on Reuters newswire text and test either on the same type of data or on general or fictional text, we demonstrate that the system exhibits log-linear increases in prediction accuracy with increasing numbers of training examples. Trained on 30 million words of newswire text, prediction accuracies range between 12.6% on fictional text and 42.2% on newswire text. In a second series of experiments we compare all-words prediction with confusable prediction, i.e., the same task, but specialized to predicting among limited sets of words. Confusable prediction yields high accuracies on nine example confusable sets in all genres of text. The confusable approach outperforms the all-words-prediction approach, but with more data the difference decreases.