A statistical approach to machine translation
Computational Linguistics
Self-organized language modeling for speech recognition
Readings in speech recognition
Class-based n-gram models of natural language
Computational Linguistics
Toward a unified approach to statistical language modeling for Chinese
ACM Transactions on Asian Language Information Processing (TALIP)
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A novel word clustering algorithm based on latent semantic analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Multi-class composite N-gram based on connection direction
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Modeling of long distance context dependency in Chinese
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Multi-speaker language modeling
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Long distance bigram models applied to word clustering
Pattern Recognition
Computational Linguistics
Long distance dependency in language modeling: an empirical study
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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The n-gram model is a stochastic model, which predicts the next word (predicted word) given the previous words (conditional words) in a word sequence. The cluster n-gram model is a variant of the n-gram model in which similar words are classified in the same cluster. It has been demonstrated that using different clusters for predicted and conditional words leads to cluster models that are superior to classical cluster models which use the same clusters for both words. This is the basis of the asymmetric cluster model (ACM) discussed in our study. In this paper, we first present a formal definition of the ACM. We then describe in detail the methodology of constructing the ACM. The effectiveness of the ACM is evaluated on a realistic application, namely Japanese Kana-Kanji conversion. Experimental results show substantial improvements of the ACM in comparison with classical cluster models and word n-gram models at the same model size. Our analysis shows that the high-performance of the ACM lies in the asymmetry of the model.