Artículos de revistas
Complex networks analysis of manual and machine translations
Fecha
2008Registro en:
INTERNATIONAL JOURNAL OF MODERN PHYSICS C, v.19, n.4, p.583-598, 2008
0129-1831
10.1142/S0129183108012285
Autor
AMANCIO, Diego R.
ANTIQUEIRA, Lucas
PARDO, Thiago A. S.
COSTA, Luciano da Fontoura
OLIVEIRA JUNIOR, Osvaldo N ovais de
NUNES, Maria G. V.
Institución
Resumen
Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by NIT tools can be distinguished from their manual counterparts by means of metrics such as in-(ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better NIT tools and automatic evaluation metrics.