Abstract. Kanazawa has shown that several non-trivial classes of categorial grammars are learnable in Gold’s model. We propose in this article to adapt this kind of symbolic lear...
The present work studies clustering from an abstract point of view and investigates its properties in the framework of inductive inference. Any class S considered is given by a hyp...
John Case, Sanjay Jain, Eric Martin, Arun Sharma, ...
We present a method for modeling user navigation on a web site using grammatical inference of stochastic regular grammars. With this method we achieve better models than the previo...
Abstract. A base problem in Web information extraction is to find appropriate queries for informative nodes in trees. We propose to learn queries for nodes in trees automatically ...
Abstract. Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of th...
Abstract. Left deteministic linear languages are a subclass of the context free languages that includes all the regular languages. Recently was proposed an algorithm to identify in...
Abstract. This paper investigates the learnability of Pregroup Grammars, a context-free grammar formalism recently defined in the field of computational linguistics. In a first ...
State Merging algorithms, such as Rodney Price’s EDSM (Evidence-Driven State Merging) algorithm, have been reasonably successful at solving DFA-learning problems. EDSM, however, ...
In this paper we report experience in the use of computational grids in the domain of natural language processing, particularly in the area of information extraction, to create qu...
Parsing and Tagging are very important tasks in Natural Language Processing. Parsing amounts to searching the correct combination of grammatical rules among those compatible with a...