Class-based n-gram models of natural language
Computational Linguistics
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Ambiguity in language learning: computational and cognitive models
Ambiguity in language learning: computational and cognitive models
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Selective sampling for example-based word sense disambiguation
Computational Linguistics
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Corpus-based statistical sense resolution
HLT '93 Proceedings of the workshop on Human Language Technology
Proceedings of the 2009 International Conference on Hybrid Information Technology
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Disambiguation based on wordnet for transliteration of arabic numerals for korean TTS
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
An improved TTS model and algorithm for web voice browser
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
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Various kinds of non-text symbols appear in texts. The oral expressions of these symbols may vary with their senses. This paper proposes a three-layer classifier (TLC) which can disambiguate the senses of these symbols effectively. The layers within TLC are employed in sequence. The 1st layer is composed of two components: pattern table and decision tree. If this layer can disambiguate the sense of the target symbol, the disambiguation task stops. Otherwise the next two layers will be triggered. In such a situation, the procedure will go through the TLC. Based on the Bayesian theory, the 2nd layer adopts the voting scheme to compute the disambiguation score. Several features of token, which may affect the effectiveness of our voting scheme, are analyzed and compared with each other to achieve better accuracy. According to the algorithm confidence of sense disambiguation, the 3rd layer may exploit an alternative model to enhance the performance. Experiments show that our approaches can learn well even with only a small amount of data. The overall accuracies of training and testing sets are 99.8% and 97.5%, respectively.