Abstract. In Natural Language Processing tasks, semantical and lexical resources are of paramount importance for efficient implementations of solutions. However, availability of tools for any other language than English is fairly limited, therefore leaving the field open to improvements and new developments. This paper presents the second version of RoEmoLex, a lexicon containing approximately eleven thousand Romanian words tagged with a series of emotions and two valences. We describe the steps followed in the improvement process of the first version of the resource: the addition of new terms, and the extension of emotional concepts to finer-grained tags.
Machine health monitoring of rotating mechanical systems is an important task in manufacturing engineering. In this paper, a system for analyzing and detecting mounting defects on a rotating test rig is developed. The test rig comprises a slender shaft with a central disc, supported symmetrically by oscillating ball bearings. The shaft is driven at constant speed (with tiny variations) through a timing belt. Faults, such as the translation of the central disc along the shaft, the disc eccentricity, and defects on the motor reducer position or timing belt mounting position, are imposed. Time and frequency domain features, extracted from the vibration signal, are used as predictors in fault detection. This task is modeled as a multi-class classification problem, where the classes correspond to eight health states: one healthy and seven faulty. Data analysis, using unsupervised and supervised algorithms, provides significant insights (relevance of features, correlation between features, classification difficulties, data visualization) into the initial dataset, a balanced one. The experiments are performed using classifiers from MATLAB and six feature sets. Quadratic SVM achieves the best performance: 99.18% accuracy for the set of all 41 features extracted from X and Y accelerometer axes, and 98.93% accuracy for the subset of the 18 most relevant features.
Emotions play a central role in both writing and understanding literary works, and poetry is a genre rich in emotional content, vivid imagery and abstract language. This paper proposes a clustering-based approach to unsupervisedly mine emotional patterns in Mihai Eminescu's poetry. Lexicon-based emotion features are used for the clustering algorithm.Resulting clusters are assessed with regard to manually added characteristics of poems in the form of literary themes. There is a partial overlap between affective and thematic content, consistent with literary evaluations of the same works. Computational approaches have the advantage of being objective and replicable, with unsupervised techniques such as clustering representing a valuable tool in the exploration of literary works. Nonetheless, no specific emotional patterns, as determined by the proposed method, can be fully associated with particular literary themes.
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