A noun-compound is a compressed proposition that requires an audience to recover the implicit relationship between two concepts that are expressed as nouns. Listeners recover this relationship by considering the most typical relations afforded by each concept. These relational possibilities are evident at a linguistic level in the syntagmatic patterns that connect nouns to the verbal actions that act upon, or are facilitated by, these nouns. We present a model of noun-compound interpretation that first learns the relational possibilities for individual nouns from corpora, and which then uses these to hypothesize about the most likely relationship that underpins a noun compound.

We present a brief overview of the main challenges in understanding the semantics of noun compounds and consider some known methods. We introduce a new task to be part of SemEval-2010: the interpretation of noun compounds using paraphrasing verbs and prepositions. The task is meant to provide a standard testbed for future research on noun compound semantics. It should also promote paraphrase-based approaches to the problem, which can benefit many NLP applications.

In the present research, an electro-oxidation method was applied to decrease the organic compounds and remove the available micro-organisms in activated sludge of the sewage. Within this method, low cost electrodes were used, including stainless steel, graphite, and Pb/PbO2, and the operating parameters (pH, current density, and operating time) were experimentally optimized. In order to determine sludge stabilization (removal of organic matters and microorganisms), the decrease of parameters like chemical oxygen demand, the increase of electroconductivity and the total dissolved solids, total coli form, and fecal coli form were investigated. Two machine learning techniques (artificial neural networks and support vector machines) were applied comparatively for prediction of the process efficiency. Accurate results were obtained by simulation, in agreement with experimental data.

A quantitative structure–property relationships analysis applied on a large dataset of aromatic organic compounds was performed using a methodology based on support vector machine (SVM). The liquid‐crystalline property was correlated with a series of structural and geometric molecular descriptors: length of the rigid and flexible sections, molecular mass, and an asymmetry parameter that quantifies the ratio between the total length and molecule diameter. The SVM classification was achieved, analyzing the influence of characteristic elements of this tool: type of kernel and parameters of the method. A comparison with other techniques (eager learners and lazy learners algorithms and neural networks) was made using the same dataset and working conditions. The accurate results provided by the SVM method, the small number of parameters, and ease of handling recommend the SVM tool as an appropriate technique for classification. Copyright © 2013 John Wiley & Sons, Ltd.

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