Using metrics from complex networks to evaluate machine translation

Autor(es)

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]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 390, n. 1, p. 131-142, JAN 1 2011.
• Citações Web of Science: 26

Resumo

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)

• Processo FAPESP: 10/00927-9 - Classificação de textos com redes complexas
• Beneficiário: Diego Raphael Amancio
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto