| Publisher | Microsoft | ||
|---|---|---|---|
| Format | 127.7KB PDF | Date added | 10 Jun 2005 |
| Topics | Programming Languages, Software Engineering | ||
| Downloads | 29 | ||
This paper presents an empirical study on four techniques of language model adaptation, including a Maximum A Posteriori (MAP) method and three discriminative training models, in the application of Japanese Kana-Kanji conversion. The authors compare the performance of these methods from various angles by adapting the baseline model to four adaptation domains. In particular, they attempt to interpret the results given in terms of the Character Error Rate (CER) by correlating them with the characteristics of the adaptation domain measured using the information-theoretic notion of cross entropy.
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