The mathematics of inheritance systems
The mathematics of inheritance systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Foundations of Computational Linguistics: Man-Machine Communication in Natural Language
Foundations of Computational Linguistics: Man-Machine Communication in Natural Language
Machine Learning
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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
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The high frequency of the use of Arabic numerals in informative texts and their multiple senses and readings deteriorate the accuracy of TTS systems. This paper presents a hybrid word sense disambiguation method exploiting a tagged corpus and a Korean wordnet, KorLex 1.0, for the correct and efficient conversion of Arabic numerals into Korean phonemes according to their senses. Individual contextual features are extracted from the tagged corpus and are grouped in order to determine the sense of Arabic numerals. Least upper bound synsets among common hypernyms of contextual features were obtained from the KorLex hierarchy, and they were used as semantic categories of the contextual features of Arabic numerals. The semantic classes were trained to classify the meaning and the reading of Arabic numerals using decision tree and to compose grapheme-to-phoneme rules for an automatic transliteration system for Arabic numerals. The proposed system outperforms the customized TTS systems by 3.9%--20.3%.