Rafael C. Carrasco

Learning Stochastic Regular Grammars by Means of a State Merging Method

We propose a new algorithm which allows for the identification of any stochastic deterministic regular language as well as the determination of the probabilities of the strings in the language. The algorithm builds the prefix tree acceptor from the sample set and merges systematically equivalent states. Experimentally, it proves very fast and the time needed grows only linearly with the size of the sample set.

Probabilistic finite-state machines - part II

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition or in fields to which pattern recognition is linked. In part I of this paper, we surveyed these objects and studied their properties. In this part, we study the relations between probabilistic finite-state automata and other well-known devices that generate strings like hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.

Learning deterministic regular grammars from stochastic samples in polynomial time

In this paper, the identification of stochastic regular languages is addressed. For this purpose, we propose a class of algorithms which allow for the identification of the structure of the minimal stochastic automaton generating the language. It is shown that the time needed grows only linearly with the size of the sample set and a measure of the complexity of the task is provided. Experimentally, our implementation proves very fast for application purposes.

A fast branch & bound nearest neighbour classifier in metric spaces

The recently introduced algorithm LAESA finds the nearest neighbour prototype in a metric space. The average number of distances computed in the algorithm does not depend on the number of prototypes but it shows linear space and time complexities. In this paper, a new algorithm (TLAESA) is proposed which has a sublinear time complexity and keeps the other features unchanged.

Open-Source portuguese–spanish machine translation

This paper describes the current status of development of an open-source shallow-transfer machine translation (MT) system for the [European] Portuguese

Incremental construction and maintenance of minimal finite-state automata

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Learning the initial state of a second-order recurrent neural network during regular-language inference

Spanish language pair, developed using the OpenTrad Apertium MT toolbox (www.apertium.org). Apertium uses finite-state transducers for lexical processing, hidden Markov models for part-of-speech tagging, and finite-state-based chunking for structural transfer, and is based on a simple rationale: to produce fast, reasonably intelligible and easily correctable translations between related languages, it suffices to use a MT strategy which uses shallow parsing techniques to refine word-for-word MT. This paper briefly describes the MT engine, the formats it uses for linguistic data, and the compilers that convert these data into an efficient format used by the engine, and then goes on to describe in more detail the pilot Portuguese

Stable Encoding of Finite-State Machines in Discrete-Time Recurrent Neural Nets with Sigmoid Units

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Stochastic Inference of Regular Tree Languages

Spanish linguistic data.

Accurate computation of the relative entropy between stochastic regular grammars

Daciuk et al. [Computational Linguistics 26(1):316 (2000)] describe a method for constructing incrementally minimal, deterministic, acyclic finite-state automata (dictionaries) from sets of strings. But acyclic finite-state automata have limitations: For instance, if one wants a linguistic application to accept all possible integer numbers or Internet addresses, the corresponding finite-state automaton has to be cyclic. In this article, we describe a simple and equally efficient method for modifying any minimal finite-state automaton (be it acyclic or not) so that a string is added to or removed from the language it accepts; both operations are very important when dictionary maintenance is performed and solve the dictionary construction problem addressed by Daciuk et al. as a special case. The algorithms proposed here may be straightforwardly derived from the customary textbook constructions for the intersection and the complementation of finite-state automata; the algorithms exploit the special properties of the automata resulting from the intersection operation when one of the finite-state automata accepts a single string.