UMUCorpusClassifierCompilation and evaluation of linguistic corpus for Natural Language Processing tasks

  1. José Antonio García Díaz
  2. Ángela Almela Sánchez-Lafuente
  3. Gema Alcaraz Mármol
  4. Rafael Valencia García
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2020

Issue: 65

Pages: 139-142

Type: Article

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Abstract

The development of an annotated corpus is a very time-consuming task. Although some researchers have proposed the automatic annotation of a corpus based on ad-hoc heuristics, valid hypotheses cannot always be made. Even when the annotation process is performed by human annotators, the quality of the corpus is heavily influenced by disagreements between annotators or with themselves. Therefore, the lack of supervision of the annotation process can lead to poor quality corpus. In this work, we propose a demonstration of UMUCorpusClassifier, a NLP tool for aid researches for compiling corpus as well as coordinating and supervising the annotation process. This tool eases the daily supervision process and permits to detect deviations and inconsistencies during early stages of the annotation process.

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Funding information

This demonstration has been supported by the Spanish National Research Agency (AEI) and the European Regional De velopment Fund (FEDER/ERDF) through projects KBS4FIA (TIN2016-76323-R) and LaTe4PSP (PID2019-107652RB-I00). In ad dition, JoséAntonio Garćıa-Díaz has been supported by Banco Santander and University of Murcia through the Doctorado industrial programme.

Funders

Bibliographic References

  • Apolinardo-Arzube, O., J. A. García-Díaz, J. Medina-Moreira, H. Luna-Aveiga, and R. Valencia-Garc´ıa. 2019. Evaluating information-retrieval models and machine-learning classifiers for measuring the social perception towards infectious diseases. Applied Sciences, 9(14):2858.
  • García-Díaz, J. A., M. Cánovas-García, and R. Valencia-García. 2020. Ontologydriven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in latin america. Future Generation Computer Systems, 112:614–657.
  • Go, A., R. Bhayani, and L. Huang. 2009. Twitter sentiment classification using distant supervision. CS224N project report, Stanford, 1(12):2009.
  • Grave, E., P. Bojanowski, P. Gupta, A. Joulin, and T. Mikolov. 2018. Learning word vectors for 157 languages. arXiv preprint arXiv:1802.06893.
  • Krippendorff, K. 2018. Content analysis: An introduction to its methodology. Sage publications.
  • Medina-Moreira, J., J. A. García-Díaz, O. Apolinardo-Arzube, H. Luna-Aveiga, and R. Valencia-García. 2019. Mining twitter for measuring social perception towards diabetes and obesity in central america. In International Conference on Technologies and Innovation, pages 81–94. Springer.
  • Medina-Moreira, J., J. O. Salavarria-Melo, K. Lagos-Ortiz, H. Luna-Aveiga, and R. Valencia-García. 2018. Opinion mining for measuring the social perception of infectious diseases. an infodemiology approach. In Proceedings of the Technologies and Innovation: 4th International Conference, CITI, page 229. Springer.
  • Mozetiˇc, I., M. Grˇcar, and J. Smailovi´c. 2016. Multilingual twitter sentiment classification: The role of human annotators. PloS one, 11(5).
  • Pak, A. and P. Paroubek. 2010. Twitter as a corpus for sentiment analysis and opinion mining. In LREc, volume 10, pages 1320–1326.
  • Salas-Zárate, M. d. P., M. A. ParedesValverde, M. A. Rodríguez-García, R. Valencia-García, and G. AlorHernández. 2017. Automatic detection of satire in twitter: A psycholinguistic-based approach. Knowl. Based Syst., 128:20–33.
  • Singh, A., N. Thakur, and A. Sharma. 2016. A review of supervised machine learning algorithms. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pages 1310–1315. Ieee.
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