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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Using metrics from complex networks to evaluate machine translation

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Author(s):
Amancio, D. R. [1] ; Nunes, M. G. V. [2] ; Oliveira, Jr., O. N. [1] ; Pardo, T. A. S. [2] ; Antiqueira, L. [1] ; Costa, L. da F. [1]
Total Authors: 6
Affiliation:
[1] Univ Sao Paulo, Inst Phys Sao Carlos, BR-13560970 Sao Paulo - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, BR-13560970 Sao Paulo - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 390, n. 1, p. 131-142, JAN 1 2011.
Web of Science Citations: 26
Abstract

Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex networks. Specifically, we extend our previous work by employing additional metrics of complex networks, whose results were used as input for machine learning methods and allowed MT texts of distinct qualities to be distinguished. Also shown is that the node-to-node mapping between source and target texts (English-Portuguese and Spanish-Portuguese pairs) can be improved by adding further hierarchical levels for the metrics out-degree, in-degree, hierarchical common degree, cluster coefficient, inter-ring degree, intra-ring degree and convergence ratio. The results presented here amount to a proof-of-principle that the possible capturing of a wider context with the hierarchical levels may be combined with machine learning methods to yield an approach for assessing the quality of MT systems. (C) 2010 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 10/00927-9 - Using complex networks to classify texts
Grantee:Diego Raphael Amancio
Support type: Scholarships in Brazil - Doctorate (Direct)