Tagging English text with a probabilistic model
Computational Linguistics
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Automatic Word Segmentation for Chinese Classics of Tea Based on Tree-Pruning
KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 01
Equations for part-of-speech tagging
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
A novel word segmentation approach for written languages with word boundary markers
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Automatic word spacing of erroneous sentences in mobile devices with limited hardware resources
Information Processing and Management: an International Journal
Automatic Korean word spacing using Pegasos algorithm
Information Processing and Management: an International Journal
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Automatic word spacing decides the correct boundaries between words in a sentence. Word spacing is important in Korean, and word spacing errors are frequent. Several proposed probabilistic word-spacing models resolve problems with previous statistical approaches. These models regard automatic word spacing as a classification problem similar to part-of-speech tagging. By generalizing hidden Markov models, the models can consider a broader context and estimate more accurate probabilities. The authors tested these models under a wide range of conditions to compare them with the state of the art and performed detailed error analysis of them.