Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Learning to predict code-switching points
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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In this paper, we investigate the adaptation of language modeling for conversational Mandarin-English Code-Switching (CS) speech and its effect on speech recognition performance. First, we investigate the prediction of code switches based on textual features with focus on Part-of-Speech (POS) tags. We show that the switching attitude is speaker dependent and utilize this finding to cluster the training speakers into classes with similar switching attitude. Second, we apply recurrent neural network language models which integrate the POS information into the input layer and factorize the output layer into languages for modeling CS. Furthermore, we adapt the background N-Gram and RNN language model to the different Code-Switching attitudes of the speaker clusters which lead to significant reductions in terms of perplexity. Finally, using these adapted language models we rerun the speech recognition system for each speaker and achieve improvements in terms of mixed error rate.