Search results for: TEXTUAL CLASSIFICATION - Bridge of Knowledge

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Search results for: TEXTUAL CLASSIFICATION

Search results for: TEXTUAL CLASSIFICATION

  • Methodology for Text Classification using Manually Created Corpora-based Sentiment Dictionary

    Publication

    - Year 2018

    This paper presents the methodology of Textual Content Classification, which is based on a combination of algorithms: preliminary formation of a contextual framework for the texts in particular problem area; manual creation of the Hierarchical Sentiment Dictionary (HSD) on the basis of a topically-oriented Corpus; tonality texts recognition via using HSD for analysing the documents as a collection of topically completed fragments...

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  • Contextual ontology for tonality assessment

    Publication

    classification tasks. The discussion focuses on two important research hypotheses: (1) whether it is possible to construct such an ontology from a corpus of textual document, and (2) whether it is possible and beneficial to use inferencing from this ontology to support the process of sentiment classification. To support the first hypothesis we present a method of extraction of hierarchy of contexts from a set of textual documents...

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  • Text classifiers for automatic articles categorization

    Publication

    The article concerns the problem of automatic classification of textual content. We present selected methods for generation of documents representation and we evaluate them in classification tasks. The experiments have been performed on Wikipedia articles classified automatically to their categories made by Wikipedia editors.

  • Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations

    Publication

    - Information - Year 2018

    The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discriminant and probabilistic methods for LSR revealing and customizing the sentiment classification process (SCP) to the more accurate recognition of text tonality. This objective was achieved...

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  • Study of Statistical Text Representation Methods for Performance Improvement of a Hierarchical Attention Network

    To effectively process textual data, many approaches have been proposed to create text representations. The transformation of a text into a form of numbers that can be computed using computers is crucial for further applications in downstream tasks such as document classification, document summarization, and so forth. In our work, we study the quality of text representations using statistical methods and compare them to approaches...

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  • Increasing K-Means Clustering Algorithm Effectivity for Using in Source Code Plagiarism Detection

    Publication
    • P. Hrkút
    • M. Ďuračík
    • M. Mikušová
    • M. Callejas-cuervo
    • J. Żukowska

    - Year 2019

    The problem of plagiarism is becoming increasingly more significant with the growth of Internet technologies and the availability of information resources. Many tools have been successfully developed to detect plagiarisms in textual documents, but the situation is more complicated in the field of plagiarism of source codes, where the problem is equally serious. At present, there are no complex tools available to detect plagiarism...

  • Method of Decision-Making Logic Discovery in the Business Process Textual Data

    Publication

    - Year 2019

    Growing amount of complexity and enterprise data creates a need for novel business process (BP) analysis methods to assess the process optimization opportunities. This paper proposes a method of BP analysis while extracting the knowledge about Decision-Making Logic (DML) in a form of taxonomy. In this taxonomy, researchers consider the routine, semi-cognitive and cognitive DML levels as functions of BP conceptual aspects of Resources,...

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  • Identification of category associations using a multilabel classifier

    Description of the data using categories allows one to describe it on a higher abstraction level. In this way, we can operate on aggregated groups of the information, allowing one to see relationships that do not appear explicit when we analyze the individual objects separately. In this paper we present automatic identification of the associations between categories used for organization of the textual data. As experimental data...

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