%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.13.19.25
%2 sid.inpe.br/sibgrapi/2021/09.13.19.25.26
%T A Comparative Study of Text Document Representation Approaches Using Point Placement-based Visualizations
%D 2021
%A Carvalho, Hevelyn Sthefany Lima de,
%A Borges, Vinicius Ruela Pereira,
%@affiliation University of Brasília
%@affiliation University of Brasília
%E Paiva, Afonso,
%E Menotti, David,
%E Baranoski, Gladimir V. G.,
%E Proença, Hugo Pedro,
%E Junior, Antonio Lopes Apolinario,
%E Papa, João Paulo,
%E Pagliosa, Paulo,
%E dos Santos, Thiago Oliveira,
%E e Sá, Asla Medeiros,
%E da Silveira, Thiago Lopes Trugillo,
%E Brazil, Emilio Vital,
%E Ponti, Moacir A.,
%E Fernandes, Leandro A. F.,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K visualization, word-embedding, feature extraction, text, multidimensional scaling.
%X In natural language processing, text representation plays an important role which can affect the performance of language models and machine learning algorithms. Basic vector space models, such as the term frequency-inverse document frequency, became popular approaches to represent text documents. In the last years, approaches based on word embeddings have been proposed to preserve the meaning and semantic relations of words, phrases and texts. In this paper, we focus on studying the influences of different text representations to the quality of layouts generated by state-of-art visualizations based on point placement. For that purpose, a visualization-assisted approach is proposed to support users when exploring such representations in classification tasks. Experimental results using two public labeled corpora were conducted to assess the quality of the layouts and to discuss possible relations to the classification performances. The results are promising, indicating that the proposed approach can guide users to understand the relevant patterns of a corpus in each representation.
%@language en
%3 WUW-9.pdf