A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences

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Language models assign probabilities to strings of symbols. Their interpretation is reviewed and applied to text classi cation. A language recogniser is constructed from Bayes' theorem and a simple bigram model. This provides

Abstract A goal of statistical language modeling is to learn the joint probabilityfunction of sequences of words. This is intrinsically difficult because of the curse of dimensionality: we propose to fight it with its own weapons.In the proposed approach one learns simultaneously (1) a distributed

Abstract A goal of statistical language modeling is to learn the joint probabilityfunction of sequences of words. This is intrinsically difficult because of the curse of dimensionality: we propose to fight it with its own weapons.In the proposed approach one learns simultaneously (1) a distributed

This paper gives the system description of the neural probabilistic language modeling (NPLM) team of Dublin City University for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT (ML4HMT-12

This paper proposes a new framework of probabilistic language modeling that satisfies the two basic requirements of: (a) integration of part-of-speech n-gram statistics, structural preference and lexical sensitivity, and (b) maintenance of their modularity. Our framework consists of the syntactic

In this thesis, we introduce probabilistic models to rank the likelihood of resultant parses within the GLR parsing framework. Probabilistic models can also bring about the benefit of reduction of search space, if the models allow prefix probabilities for partial parses. In devising the models, we

In this paper, we introduce a restricted form of phrase structure grammar to handle the characteristics of Korean more efficiently. Based on this restricted form of the grammar, we propose a probabilistic parser for Korean sentences. To show usefulness of the parser proposed in this paper, we made

, the projections of words onto their corresponding singular vectors forms the basis of a spectral method for estimating HMMs [3, 4]. More generally, the hidden states in a variety of directed dynamic Bayes nets, including HMMs and probabilistic grammars, can be estimated using spectral methods. These methods