Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

An environmentally friendly analytical procedure for nickel determination by atomic and molecular spectrometry after cloud point extraction in different samples

Cloud point extraction (CPE) was employed for separation and preconcentration prior to the determination of nickel by graphite furnace atomic absorption spectrometry (GFAAS), flame atomic absorption spectrometry (FAAS) or UV-Vis spectrophotometry. Di-2-pyridyl ketone salicyloylhydrazone (DPKSH) was used for the first time as a complexing agent in CPE. The nickel complex was extracted from the aqueous phase using the Triton X-114 surfactant. Under optimized conditions, limits of detection obtained with GFAAS, FAAS and UV-Vis spectrophotometry were 0.14, 0.76 and 1.5 mu g L-1, respectively. The extraction was quantitative and the enrichment factor was estimated to be 27. The method was applied to natural waters, hemodialysis concentrates, urine and honey samples. Accuracy was evaluated by analysis of the NIST 1643e Water standard reference material.