The Algorithm of Modelling and Analysis of Latent Semantic Relations: Linear Algebra vs. Probabilistic Topic Models - Publication - MOST Wiedzy

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The Algorithm of Modelling and Analysis of Latent Semantic Relations: Linear Algebra vs. Probabilistic Topic Models

Abstract

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 analysis of the particular paragraphs. The algorithm contains the following stages: modelling and analysis of Latent Semantic Relations consistently on LDA- and LSA-based levels; rules-based adjustment of the results of the two levels of analysis. The verification of the proposed algorithm for subjectively positive and negative Polish-language film reviews corpuses was conducted. The level of the recall rate and precision indicator, as a result of case study, allowed to draw the conclusions about the effectiveness of the proposed algorithm

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Copyright (Springer International Publishing AG 2017)

Details

Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Language:
English
Publication year:
2017
Bibliographic description:
Rizun N., Waloszek W., Yurii T.: The Algorithm of Modelling and Analysis of Latent Semantic Relations: Linear Algebra vs. Probabilistic Topic Models// Knowledge Engineering and Semantic Web/ ed. Różewski P., Lange C. : Springer, Cham, 2017, s.53-68
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-319-69548-8_5
Verification:
Gdańsk University of Technology

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