Maximum-Likelihood Language Model
A Maximum-Likelihood Language Model is a likelihood model that ...
- See: Maximum-Likelihood Training Algorithm, Unsmoothed Maximum-Likelihood Training Algorithm, Language Model Training Algorithm.
References
2004
- (Zhai & Lafferty, 2004) ⇒ Chengxiang Zhai, and John Lafferty. (2004). “A Study of Smoothing Methods for Language Models Applied to Information Retrieval.” In: ACM Transactions on Information Systems (TOIS) Journal, 22(2). doi:10.1145/984321.984322
- QUOTE: … The basic idea of these approaches is to estimate a language model for each document, and to then rank documents by the likelihood of the query according to the estimated language model. A central issue in language model estimation is smoothing, the problem of adjusting the maximum likelihood estimator to compensate for data sparseness. In this article, we study the problem of language model smoothing and its influence on retrieval performance. ...
2003
- (Och, 2003) ⇒ Franz Josef Och. (2003). “Minimum Error Rate Training in Statistical Machine Translation.” In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1. doi:10.3115/1075096.1075117
- QUOTE: Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text.