Abstract
In Mongolian language, there is a phenomenon that many words have the same presentation form but represent different words with different codes. Since typists usually input the words according to their representation forms and cannot distinguish the codes sometimes, there are lots of coding errors occurred in Mongolian corpus. It results in statistic and retrieval very difficult on such a Mongolian corpus. To solve this problem, this paper proposed a method which merges the words with same presentation forms by Intermediate characters, then use the corpus in Intermediate characters form to build Mongolian language model. Experimental result shows that the proposed method can reduce the perplexity and the word error rate for the 3-gram language model by 41 % and 30 % respectively when comparing model trained on the corpus without processing. The proposed approach significantly improves the performance of Mongolian language model and greatly enhances the accuracy of Mongolian speech recognition.
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Acknowledgements
This research was partially supported by the China National Nature Science Foundation (No. 61263037 and No. 61563040), Inner Mongolia nature science foundation (No. 2014BS0604) and the program of high-level talents of Inner Mongolia University.
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Yan, X., Bao, F., Wei, H., Su, X. (2016). A Novel Approach to Improve the Mongolian Language Model Using Intermediate Characters. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_9
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DOI: https://doi.org/10.1007/978-3-319-47674-2_9
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