Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A simple, fast, and effective rule learner
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
Natural Language Grammatical Inference with Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Text chunking by combining hand-crafted rules and memory-based learning
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
A hybrid approach to automatic word-spacing in Korean
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
Short message service (SMS) is a widely used service in modern mobile phones that allows users to send or receive short text messages. Current SMS, however, has two problems of inconvenient input and short message length. These problems can be resolved if a phone has an ability of automatic word spacing. This is because users need not put spaces in sending messages and longer messages are possible as they contain no space. Thus, automatic word spacing will be a very useful tool for SMS, if it can be commercially served. The practical issues of implementing it on the devices such as mobile phones are small memory and low computing power of the devices. To tackle these problems, this paper proposes a combined model of rule-based learning and memory-based learning. According to the experimental results, the model shows higher accuracy than rule-based learning or memory-based learning alone. In addition, the generated rules are so small and simple that the proposed model is appropriate for small memory devices.