Smoothing and compression with stochastic k-testable tree languages

A probabilistic extension of locally testable tree languages

Probabilistic k-testable models (usually known as k-gram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events. In this paper we introduce the family of stochastic k-testable tree languages and describe how these models can approximate any stochastic rational tree language. This is applied, as a particular case, to the...

K-TLSS(S) language models for speech recognition

The class of K-Testable Languages in the Strict Sense (K-TLSS) is a subclass of regular languages. Previous works demonstrate that stochastic K-TLSS language models describe the same probability distribution as N-gram models, and that smoothing techniques can be e ciently applied (Back-o like methods). Once we have a set of k-TLSS models (k = 1 : : :K) and a smoothing technique that specificall...

Stochastic k-testable Tree Languages and Applications

Learning k-Testable tree sets from positive data

A k-Testable tree set in the Strict sense (k-TS) is essentially defined by a finite set of patterns of "size" k that are permitted to appear in the trees of the tree language. Given a positive sample S of trees over a ranked alphabet, an algorithm is proposed which obtains the smallest k-TS tree set containing S. The proposed algorithm is polynomial on the size of S and identifies the class of ...

Stochastic K-TSS Bi-Languages for Machine Translation

One of the approaches to statistical machine translation is based on joint probability distributions over some source and target languages. In this work we propose to model the joint probability distribution by stochastic regular bi-languages. Specifically we introduce the stochastic k-testable in the strict sense bi-languages to represent the joint probability distribution of source and target...