On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models
Joan
Andreu
Sánchez
author
Martha
Alicia
Rocha
author
Verónica
Romero
author
Mauricio
Villegas
author
2018-apr
text
journal article
Computational Linguistics
continuing
MIT Press
Cambridge, MA
periodical
academic journal
Probabilistic finite-state automata are a formalism that is widely used in many problems of automatic speech recognition and natural language processing. Probabilistic finite-state automata are closely related to other finite-state models as weighted finite-state automata, word lattices, and hidden Markov models. Therefore, they share many similar properties and problems. Entropy measures of finite-state models have been investigated in the past in order to study the information capacity of these models. The derivational entropy quantifies the uncertainty that the model has about the probability distribution it represents. The derivational entropy in a finite-state automaton is computed from the probability that is accumulated in all of its individual state sequences. The computation of the entropy from a weighted finite-state automaton requires a normalized model. This article studies an efficient computation of the derivational entropy of left-to-right probabilistic finite-state automata, and it introduces an efficient algorithm for normalizing weighted finite-state automata. The efficient computation of the derivational entropy is also extended to continuous hidden Markov models.
sanchez-etal-2018-derivational
10.1162/COLI_a_00306
https://aclanthology.org/J18-1002
2018-apr
44
1
17
37