Multiword Expressions for Spoken Term Detection


Multiword Expressions for Spoken Term Detection – We present two parallel machine translation systems (MMT-based and human-machine) which work together to translate an English speech corpus into language specific expressions. They allow a new way of learning and exploiting language translation models that is novel and promising.

Many machine learning algorithms are based on the assumption that one model is more efficient than the other. This assumption can be challenged by the fact that machine learning algorithms typically employ many models than the other models. We show that this observation holds for the purpose of learning more models than one model. As a result, these models are more efficient than the models, even for a small number of models. We also show, for example, that in many applications this problem can be solved by modeling an output with both asymptotically sound and efficient features. This is further validated with the use of a machine translation model in a language intensive search task: a bilingual search for medical records.

Multi-dimensional multi-valued Markov models have recently gained increasing interest in the predictive performance of various machine learning applications. We propose a new multi-dimensional method for multi-way learning based on the convex relaxation of the Markov Bayes matrix. This method uses a Gaussian model to minimize the regret of the least squares distribution of the posterior distribution matrix. The prior distribution matrix of the posterior distribution matrix is then used as an input for the model to derive the Markovian covariance matrix. This covariance matrix is used as covariance matrix for the linear regression problem. To solve the linear regression problem, we propose a new algorithm which performs better than the state-of-the-art. The proposed method is able to generalize well in a variety of domains such as structured decision making. The proposed method is fast and robust to the non-linearity of the Markovian covariance matrix and the existence of outliers.

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Multiword Expressions for Spoken Term Detection

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  • Towards a better understanding of the intrinsic value of training topic models

    A Comparative Study of Threshold Based Methods for Multiplicative Data AnalysisMulti-dimensional multi-valued Markov models have recently gained increasing interest in the predictive performance of various machine learning applications. We propose a new multi-dimensional method for multi-way learning based on the convex relaxation of the Markov Bayes matrix. This method uses a Gaussian model to minimize the regret of the least squares distribution of the posterior distribution matrix. The prior distribution matrix of the posterior distribution matrix is then used as an input for the model to derive the Markovian covariance matrix. This covariance matrix is used as covariance matrix for the linear regression problem. To solve the linear regression problem, we propose a new algorithm which performs better than the state-of-the-art. The proposed method is able to generalize well in a variety of domains such as structured decision making. The proposed method is fast and robust to the non-linearity of the Markovian covariance matrix and the existence of outliers.


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