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Rafael C Carrasco

Publications and source records attributed to Rafael C Carrasco.

3 recordsLinked to original sources

Parsing with probabilistic strictly 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 during pattern classification. 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. The model is applied to the task of learning a probabilistic k-testable model from a sample of parsed sentences. In particular, a parser for a natural language grammar that incorporates smoothing is shown.

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Probabilistic finite-state machines--part I.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their definitions and properties. In Part II, we will study the relation of probabilistic finite-state automata with other well-known devices that generate strings as hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.

Algorithms↗

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 II, 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.

Algorithms↗