A hierarchical Dirichlet language model

A hierarchical Dirichlet language model

We discuss a hierarchical probabilistic model whose predictions are similar to those of the popular language modelling procedure known as 'smoothing'. A number of interesting differences from smoothing emerge. The insights gained from a probabilistic view of this problem point towards new directions for language modelling. The ideas of this paper are also applicable to other problems such as th...

Hierarchical pitman-yor and dirichlet process for language model

This paper presents a nonparametric interpretation for modern language model based on the hierarchical Pitman-Yor and Dirichlet (HPYD) process. We propose the HPYD language model (HPYD-LM) which flexibly conducts backoff smoothing and topic clustering through Bayesian nonparametric learning. The nonparametric priors of backoff n-grams and latent topics are tightly coupled in a compound process....

A Hierarchical Model of Language

Tractography Segmentation Using a Hierarchical Dirichlet Processes Mixture Model

In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. The number of clusters is automatically learnt from data with a Dirichlet process (DP) prior instead of being manually specified. After the models of bundles have been learnt from training data without supervision, they...

Update Summarization using a Multi-level Hierarchical Dirichlet Process Model

Update summarization is a new challenge which combines salience ranking with novelty detection. Previous researches usually convert novelty detection to the problem of redundancy removal or salience re-ranking, and seldom explore the birth, splitting, merging and death of aspects for a given topic. In this paper, we borrow the idea of evolutionary clustering and propose a three-level HDP model ...