Maximum Likelihood

AbstractIn this paper we describe a language recognition algorithm for multilingual documents that is based on mixed-order n-grams, Markov chains, maximum likelihood, and dynamic programming. We present the results of …...

Often, the training procedure for statisti- cal machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a …...

This paper describes the four entries from the Uni- versity of Utah in the semantic role labeling task of SENSEVAL-3. All the entries took a statisti- cal machine learning approach, …...

Arabic morphology is complex, partly be- cause of its richness, and partly because of common irregular word forms, such as broken plurals (which resemble singular nouns), and nouns with irregular …...

We present a unified unsupervised statistical model for text normalization. The relation- ship between standard and non-standard to- kens is characterized by a log-linear model, permitting arbitrary features. The weights …...

Many models in NLP involve latent vari- ables, such as unknown parses, tags, or alignments. Finding the optimal model pa- rameters is then usually a difficult noncon- vex optimization problem. …...

AbstractDespite some empirical success at correcting exposure bias in machine translation, scheduled sampling algorithms suffer from a major drawback: they incorrectly assume that words in the reference translations and in …...

AbstractStructured prediction is often approached by training a locally normalized model with maximum likelihood and decoding approximately with beam search. This approach leads to mismatches as, during training, the model …...