Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Statistical Language Learning
Naive Bayesian Classifier Committees
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Automatic word spacing in Korean for small memory devices
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Automatic word spacing using hidden Markov model for refining Korean text corpora
COLING '02 Proceedings of the 3rd workshop on Asian language resources and international standardization - Volume 12
Self-organizing η-gram model for automatic word spacing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automatic Word Spacing Using Probabilistic Models Based on Character n-grams
IEEE Intelligent Systems
IEEE Transactions on Information Theory
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With the rapid evolution of the mobile environment, the demand for natural language applications on mobile devices is increasing. This paper proposes an automatic word spacing system, the first step module of natural language processing (NLP) for many languages with their own word spacing rules, that is designed for mobile devices with limited hardware resources. The proposed system uses two stages. In the first stage, it preliminarily corrects word spacing errors by using a modified hidden Markov model based on character unigrams. In the second stage, the proposed system re-corrects the miscorrected word spaces by using lexical rules based on character bigrams or longer combinations. By using this hybrid method, the proposed system improves the robustness against unknown word patterns, reduces memory usage, and increases accuracy. To evaluate the proposed system in a realistic mobile environment, we constructed a mobile-style colloquial corpus using a simple simulation method. In experiments with a commercial mobile phone, the proposed system showed good performances (a response time of 0.20s per sentence, a memory usage of 2.04MB, and an accuracy of 92-95%) in the various evaluation measures.