Непараметрическая байесовская модель

For centuries, the deep connection between languages has brought about major discover- ies about human communication. In this pa- per we investigate how this powerful source of information can be …...

We investigate the problem of unsupervised part-of-speech tagging when raw parallel data is available in a large number of languages. Patterns of ambiguity vary greatly across lan- guages and therefore …...

We propose a non-parametric Bayesian model for unsupervised semantic parsing. Follow- ing Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the …...

In this paper we present a fully unsupervised nonparametric Bayesian model that jointly in- duces POS tags and morphological segmen- tations. The model is essentially an infi- nite HMM that …...

This paper presents an incremental prob- abilistic learner that models the acquis- tion of syntax and semantics from a cor- pus of child-directed utterances paired with possible representations of their …...

AbstractWe introduce a novel nonparametric Bayesian model for the induction of Combinatory Categorial Grammars from POS-tagged text. It achieves state of the art performance on a number of languages, and …...

AbstractIn current research, most tree-based translation models are built directly from parse trees. In this study, we go in another direction and build a translation model with an unsupervised tree …...

We present a method to jointly learn fea- tures and weights directly from distri- butional data in a log-linear framework. Specifically, we propose a non-parametric Bayesian model for learning phonologi- …...

AbstractWe propose a non-parametric Bayesian model for learning and weighting symbolically-defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate …...