Complex networks analysis of manual and machine translations

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.

Measurement of the anodic potential in solid state: A tool for determination of the antioxidant potential of phytomedicine

The electroanalytical techniques are very promissing to perform the quality control of crude vegetable. Solid State Differential Pulse Voltammetry in the supporting electrolyte is able to detect the oxidation signals of the active material, which can be used as a parameter to identify the type of crude vegetable and its antioxidant activity. The working electrode consisted in a carbon paste electrode modified with the powder of vegetable raw material (EMF). The electrochemical measurements were performed in a cell containing the working (EMF), reference (Ag/AgCl, KClsat) and auxiliary (Pt) electrodes.