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The Algorithm of Modelling and Analysis of Latent Semantic Relations: Linear Algebra vs. Probabilistic Topic Models
This paper presents the algorithm of modelling and analysis of Latent Semantic Relations inside the argumentative type of documents collection. The novelty of the algorithm consists in using a systematic approach: in the combination of the probabilistic Latent Dirichlet Allocation (LDA) and Linear Algebra based Latent Semantic Analysis (LSA) methods; in considering each document as a complex of topics, defined on the basis of separate...
Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations
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...
This paper presents an approach for Modeling the Latent Semantic Relations. The approach is based on advantages of two computational approaches: Latent Semantic Analysis and Latent Dirichlet Allocation. The scientific question about the possibility of reducing the influence of these Methods limitation on the Quality of the Latent Semantic Relations Analysis Results is raised. The case study for building the Two-level Hierarchical Contextual...
Obtained scientific degrees/titles
Obtained science degreedr inż. Information science (Technology)
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