Class-Based Probability Estimation Using a Semantic Hierarchy

Class-Based Probability Estimation Using a Semantic Hierarchy

This article concerns the estimation of a particular kind of probability, namely, the probability of a noun sense appearing as a particular argument of a predicate. In order to overcome the accompanying sparse-data problem, the proposal here is to define the probabilities in terms of senses from a semantic hierarchy and exploit the fact that the senses can be grouped into classes consisting of ...

Class-Based Probability Estimation using a Semanti Hierar hy

Hiding a Semantic Class Hierarchy in a Markov Model

This paper introduces a new model of selectional preference induction. Unlike previous approaches we provide a stochastic generation model for the words that appear as arguments of a predicate. More speciically, we deene a hidden Markov model with the general shape of our semantic class hierarchy and we use an EM-like algorithm for parameter estimation. This model enables us to handle word sens...

Probability Estimation for Multi-class Classification Based on Label Ranking

We consider the problem of probability estimation in the setting of multi-class classification. While this problem has already been addressed in the literature, we tackle it from a novel perspective. Exploiting the close connection between probability estimation and ranking, our idea is to solve the former on the basis of the latter, taking advantage of recently developed methods for label rank...

Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior

We introduce a new method for building classification models when we have prior knowledge of how the classes can be arranged in a hierarchy, based on how easily they can be distinguished. The new method uses a Bayesian form of the multinomial logit (MNL, a.k.a. “softmax”) model, with a prior that introduces correlations between the parameters for classes that are nearby in the tree. We compare ...