State-of-the-art models of artificial intelligence are developed in the black-box paradigm, in which sensitive information is limited to input-output interfaces, while internal representations are not interpretable. The resulting algorithms lack explainability and transparency, requested for responsible application. This paper addresses the problem by a method for finding Osgood’s dimensions of affective meaning in multidimensional space of a pre-trained word2vec model of natural language. Three affective dimensions are found based on eight semantic prototypes, composed of individual words. Evaluation axis is found in 300-dimensional word2vec space as a difference between positive and negative prototypes. Potency and activity axes are defined from six process-semantic prototypes (perception, analysis, planning, action, progress, and evaluation), representing phases of a generalized circular process in that plane. All dimensions are found in simple analytical form, not requiring additional training. Dimensions are nearly orthogonal, as expected for independent semantic factors. Osgood’s semantics of any word2vec object is then retrieved by a simple projection of the corresponding vector to the identified dimensions. The developed approach opens the possibility for interpreting the inside of black box-type algorithms in natural affective-semantic categories, and provides insights into foundational principles of distributive vector models of natural language. In the reverse direction, the established mapping opens machine-learning models as rich sources of data for cognitive-behavioral research and technology.
In the context of the ongoing forth industrial revolution and fast computer science development the amount of textual information becomes huge. So, prior to applying the seemingly appropriate methodologies and techniques to the above data processing their nature and characteristics should be thoroughly analyzed and understood. At that, automatic text processing incorporated in the existing systems may facilitate many procedures. So far, text classification is one of the basic applications to natural language processing accounting for such factors as emotions’ analysis, subject labeling etc. In particular, the existing advancements in deep learning networks demonstrate that the proposed methods may fit the documents’ classifying, since they possess certain extra efficiency; for instance, they appeared to be effective for classifying texts in English. The thorough study revealed that practically no research effort was put into an expertise of the documents in Vietnamese language. In the scope of our study, there is not much research for documents in Vietnamese. The development of deep learning models for document classification has demonstrated certain improvements for texts in Vietnamese. Therefore, the use of long short term memory network with Word2vec is proposed to classify text that improves both performance and accuracy. The here developed approach when compared with other traditional methods demonstrated somewhat better results at classifying texts in Vietnamese language. The evaluation made over datasets in Vietnamese shows an accuracy of over 90%; also the proposed approach looks quite promising for real applications.
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