Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Retrieval
Detecting and correcting speech repairs
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
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Disfluent speech adds to the difficulty of processing spoken language utterances. In this paper we concentrate on identifying one disfluency phenomenon: fragmented words. Our data, from the Spoken Dutch Corpus, samples nearly 45,000 sentences of human discourse, ranging from spontaneous chat to media broadcasts. We classify each lexical item in a sentence either as a completely or an incompletely uttered, i.e. fragmented, word. The task is carried out both by the IB 1 and RIPPER machine learning algorithms, trained on a variety of features with an extensive optimization strategy. Our best classifier has a 74.9% F-score, which is a significant improvement over the baseline. We discuss why memory-based learning has more success than rule induction in correctly classifying fragmented words.