This paper describes a unified architecture for integrating sub-lexical models with speech recognition, and a layered framework for context-dependent probabilistic hierarchical sublexical modelling. Previous work [1, 2, 3] has demonstrated the effectiveness of sub-lexical modelling using a core context-free grammar (CFG) augmented with context-dependent probabilistic models. Our major motivatio...

There have been many attempts to give a coherent formulation of a hierarchical progression that would lead to a refined partition of the vast area stretching from the context-free to the context-sensitive languages. The purpose of this note is to describe a theory that seems to afford a promising method of interpreting the tree adjoining languages as the natural third step in a hierarchy that s...

In this work we propose an approach for incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP), employed for evolution and adaptation of locomotion gaits of a simulated snake-like robot (Snakebot). Our approach is derived from the original context-free grammar which usually expresses the syntax of genetic programs in canonical GP. Empirically obtained...

A bst ract. A one-dimensional cellular automaton rule with specified boundary conditions can be considered as acting simultaneously on all finite lattices, which gives a mapping between formal languages. Regular lang uages are always mapped to regular langu ages, contex t-free to context-free, context-sensitive to context-sensitive, and recursive sets to recursive sets . In particular, the fini...

This paper introduces Stochastic Definite Clause Grammars, a stochastic variant of the wellknown Definite Clause Grammars. The grammar formalism supports parameter learning from annotated or unannotated corpora and provides a mechanism for parse selection by means of statistical inference. Unlike probabilistic contextfree grammars, it is a context-sensitive grammar formalism and it has the abil...

The Probabilistic Context-Free Grammar (PCFG) model is widely used for parsing natural languages, including Modern Chinese. But for Classical Chinese, the computer processing is just commencing. Our previous study on the part-of-speech (POS) tagging of Classical Chinese is a pioneering work in this area. Now in this paper, we move on to the PCFG parsing of Classical Chinese texts. We continue t...

The introduction of the adjoining operation to context-free grammars comes at high costs: The worst case time complexity of (Earley, 1968) is O n 3 ¡ , whereas Tree Adjoining Grammars have O n 6 ¡ ((Schabes, 1990)). Thus, avoiding adjoining as far as possible seems to be a good idea for reducing costs (e.g.) address this problem more radically by restricting the adjoining operation of TAGs such...

The formalized and algorithmic study of human language within the field of Natural Language Processing (NLP) has motivated much theoretical work in the related field of formal languages, in particular the subfields of grammar and automata theory. Motivated and informed by NLP, the papers in this thesis explore the connections between expressibility – that is, the ability for a formal system to ...

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Grammar. In this paper, we present the first LRstyle parsing algorithm for Linear ContextFree Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years.

In this work we propose an approach of incorporating learning contextsensitive grammar in strongly typed genetic programming (GP) employed for evolution and adaptation of locomotion gaits of simulated snake-like robot (Snakebot). In our approach the probabilistic context-sensitive grammar is derived from the originally defined context-free grammar (which usually expresses the syntax of genetic ...