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An Analysis of Different Text Representation Schemes for an Immune Clustering Algorithm

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Distributed Computing and Artificial Intelligence, 21st International Conference (DCAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1259))

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Abstract

This research investigates the challenges and effectiveness of various text representation methods (standard vector, grammar-based, and distributed), when applied to clustering short texts. The study explores Bag-of-Words for standard vector, Linguistic Inquiry and Word Count (LIWC), Part-of-Speech Tagging (POS-Tagging), and the Medical Research Council Psycholinguistic Database (MRC) for grammar-based, and Word2Vec, fastText, Doc2Vec, and SentenceBERT for distributed representations. Utilizing the aiNet bio-inspired clustering algorithm, the results reveal surprising findings, with grammar-based representations demonstrating competitive performance despite their simplicity, while standard vectors exhibit known challenges like high dimensionality. The study contributes insights into the properties of different text representations, providing a foundation for optimizing their application in clustering tasks with short and informal texts.

Supported by CNPq for the research grant PQ 303356/2022-7; CAPES for the projects STIC-AmSud (CAMA) No. 88881.694458/2022-01; Mackenzie-PrInt No. 88887.310281/2018-00; and FAPESP for grant 2021/11905-0.

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Author information

Authors and Affiliations

  1. FCI - PPGEEC - Programa de Pós-Graduação em Engenharia Elétrica e Computação, Universidade Presbiteriana Mackenzie, São Paulo, SP, Brazil

    Matheus A. Ferraria & Pedro P. Balbi

  2. Graduate Program in Technology, School of Technology, State University of Campinas (Unicamp), Limeira, São Paulo, SP, Brazil

    Leandro N. de Castro

  3. Human-Centered Artificial Intelligence and Natural Computing Research Group, Florida Gulf Coast University, Fort Myers, FL, 33965, USA

    Leandro N. de Castro

Authors
  1. Matheus A. Ferraria
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  2. Pedro P. Balbi
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  3. Leandro N. de Castro
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Corresponding author

Correspondence to Matheus A. Ferraria .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology University, Vellore, Tamil Nadu, India

    Ravikumar Chinthaginjala

  2. Kielce University of Technology, Kielce, Poland

    Pawel Sitek

  3. Department of Computer Science, Imam Abdulrahman Bin Faisal University (KSA), Dammam, Saudi Arabia

    Nasro Min-Allah

  4. Osaka Institute of Technology, Osaka, Japan

    Kenji Matsui

  5. University Rey Juan Carlos, Madrid, Spain

    Sascha Ossowski

  6. Biotechnology, Intelligent Systems and Educational Technology (BISITE) Research Group, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

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Ferraria, M.A., Balbi, P.P., de Castro, L.N. (2025). An Analysis of Different Text Representation Schemes for an Immune Clustering Algorithm. In: Chinthaginjala, R., Sitek, P., Min-Allah, N., Matsui, K., Ossowski, S., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 21st International Conference. DCAI 2024. Lecture Notes in Networks and Systems, vol 1259. Springer, Cham. https://doi.org/10.1007/978-3-031-82073-1_25

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