This paper treats nominal entity tagging as a six-way (five categories plus nonentity) classification problem and applies a smoothing maximum entropy (ME) model with a Gaussian prior to the Chinese nominal entity tagging task. The experimental results show that the model performs consistently better than a ME model using a simple counting cut-off. The results also suggest that simple semantic f...

We show that various formulations (e.g., dual and Kullback-Csiszar iterations) of estimation of maximum entropy (ME) models can be transformed to solving systems of polynomial equations in several variables for which one can use celebrated Gröbner bases methods. Posing of ME estimation as solving polynomial equations is possible, in the cases where feature functions (sufficient statistic) that ...

Discourse relation is an important content of discourse semantic analysis, and the study of punctuation is of importance for discourse relation. In this paper, we propose a method of Chinese comma classification based on maximum entropy (ME). This method classifies the sentence relation based on comma with ME by extracting rich linguistic features before and after the commas in sentences. Exper...

The method of maximum entropy (ME) is extended to address the following problem: Once one accepts that the ME distribution is to be preferred over all others, the question is to what extent are distributions with lower entropy supposed to be ruled out. Two applications are given. The first is to the theory of thermodynamic fluctuations. The formulation is exact, covariant under changes of coord...

This paper presents a new discriminative language model based on the whole-sentence maximum entropy (ME) framework. In the proposed discriminative ME (DME) model, we exploit an integrated linguistic and acoustic model, which properly incorporates the features from n-gram model and acoustic log likelihoods of target and competing models. Through the constrained optimization of integrated model, ...

There are two major approaches for blind separation: Maximum Entropy (ME) and Minimum Mutual Information (MMI). Both can be implemented by the stochastic gradient descent method for obtaining the de-mixing matrix. The MI is the contrast function for blind separation while the entropy is not. To justify the ME, the relation between ME and MMI is rstly elucidated by calculating the rst derivative...

There are two major approaches for blind separation: Maximum Entropy (ME) and Minimum Mutual Information (MMI). Both can be implemented by the stochastic gradient descent method for obtaining the de-mixing matrix. The MI is the contrast function for blind separation while the entropy is not. To justify the ME, the relation between ME and MMI is rstly elucidated by calculating the rst derivative...

In this paper, we consider the determination methods of maximum entropy multivariate distributions with given prior under the constraints, that the marginal distributions or the marginals and covariance matrix are prescribed. Next, some numerical solutions are considered for the cases of unavailable closed form of solutions. Finally, these methods are illustrated via some numerical examples.