............................................................................................................................................................................ 8 Introduction ...................................................................................................................................................................... 9 Olive ridley sea turtles in the eastern ...

This paper describes a hybrid system that applies maximum entropy (MaxEnt) model with Hidden Markov model (HMM) and some linguistic rules to recognize name entities in Oriya language. The main advantage of our system is, we are using both HMM and MaxEnt model successively with some manually developed linguistic rules. First we are using MaxEnt to identify name entities in Oria corpus, then tagg...

We investigate the problem of using continuous features in the maximum entropy (MaxEnt) model. We explain why the MaxEnt model with the moment constraint (MaxEnt-MC) works well with binary features but not with the continuous features. We describe how to enhance constraints on the continuous features and show that the weights associated with the continuous features should be continuous function...

This paper presents our recent work on period disambiguation, the kernel problem in sentence boundary identification, with the maximum entropy (Maxent) model. A number of experiments are conducted on PTB-II WSJ corpus for the investigation of how context window, feature space and lexical information such as abbreviated and sentence-initial words affect the learning performance. Such lexical inf...

Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-i...

The following picture shows the test omission rate and predicted area as a function of the cumulative threshold, averaged over the replicate runs. The omission rate should be close to the predicted omission, because of the definition of the cumulative threshold. The next picture is the receiver operating characteristic (ROC) curve for the same data, again averaged over the replicate runs. Note ...

MaxEnt is one of the most widely used tools in ecology, biogeography, and evolution for modeling and mapping species distributions using presence-only occurrence records and associated environmental covariates. Despite its popularity, the exponential model implemented by MaxEnt does not directly estimate occurrence probability, the natural quantity of interest when modeling species distribution...

The following picture shows the test omission rate and predicted area as a function of the cumulative threshold, averaged over the replicate runs. The omission rate should be close to the predicted omission, because of the definition of the cumulative threshold. The next picture is the receiver operating characteristic (ROC) curve for the same data, again averaged over the replicate runs. Note ...

The analysis of discrimination, feature and model selection conduct to the discussion of the relationships between Support Vector Machine (SVM), Bayesian and Maximum Entropy (MaxEnt) formalisms. MaxEnt discrimination can be seen as a particular case of Bayesian inference, which at its turn can be seen as a regularization approach applicable to SVM. Probability measures can be attached to each f...

Maximum entropy (MaxEnt) models have been used in many spoken language tasks. The training of a MaxEnt model often involves an iterative procedure that starts from an initial parameterization and gradually updates it towards the optimum. Due to the convexity of its objective function (hence a global optimum on a training set), little attention has been paid to model initialization in MaxEnt tra...