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Reference TypeConference Paper (Conference Proceedings)
Last Update2021: (UTC)
Metadata Last Update2021: (UTC)
Citation KeyCarvalhoBorg:2021:CoStTe
TitleA Comparative Study of Text Document Representation Approaches Using Point Placement-based Visualizations
Access Date2021, Oct. 19
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Author1 Carvalho, Hevelyn Sthefany Lima de
2 Borges, Vinicius Ruela Pereira
Affiliation1 University of Brasília
2 University of Brasília
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeUndergraduate Work
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
feature extraction
multidimensional scaling
AbstractIn 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.
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