Sánchez Peiró, JA.; Rocha, MA.; Romero, V.; Villegas, M. (2018). On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models. Computational Linguistics. 44(1):17-37. https://doi.org/10.1162/COLI_a_00306
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/133558
Title:
|
On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models
|
Author:
|
Sánchez Peiró, Joan Andreu
Rocha, M. A.
Romero, Verónica
Villegas, Mauricio
|
UPV Unit:
|
Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
|
Issued date:
|
|
Abstract:
|
[EN] 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 ...[+]
[EN] 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.
[-]
|
Copyrigths:
|
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
|
Source:
|
Computational Linguistics. (issn:
0891-2017
)
|
DOI:
|
10.1162/COLI_a_00306
|
Publisher:
|
MIT Press
|
Publisher version:
|
https://doi.org/10.1162/COLI_a_00306
|
Project ID:
|
info:eu-repo/grantAgreement/EC/H2020/674943/EU
CEE/600707
MINECO/TIN2012-37475-C02-01
|
Thanks:
|
This work has been partially supported through the European Union's H2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943) and the MINECO/FEDER-UE project TIN2015-70924-C2-1-R. The second author ...[+]
This work has been partially supported through the European Union's H2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943) and the MINECO/FEDER-UE project TIN2015-70924-C2-1-R. The second author was supported by the "Division de Estudios de Posgrado e Investigacion" of Instituto Tecnologico de Leon.
[-]
|
Type:
|
Artículo
|