IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms
Artificial Intelligence Review - Special issue on lazy learning
All-word prediction as the ultimate confusable disambiguation
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
HOO 2012: a report on the preposition and determiner error correction shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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We describe the Valkuil.net team entry for the HOO 2012 Shared Task. Our systems consists of four memory-based classifiers that generate correction suggestions for middle positions in small text windows of two words to the left and to the right. Trained on the Google 1TB 5-gram corpus, the first two classifiers determine the presence of a determiner or a preposition between all words in a text in which the actual determiners and prepositions are masked. The second pair of classifiers determines which is the most likely correction given a masked determiner or preposition. The hyperparameters that govern the classifiers are optimized on the shared task training data. We point out a number of obvious improvements to boost the medium-level scores attained by the system.