SciEsp: Structural Analysis of Abstracts Written in Spanish

Authors

  • Irvin Vargas-Campos Pontificia Universidad Católica del Perú
  • Fernando Alva-Manchego Pontificia Universidad Católica del Perú

DOI:

https://doi.org/10.13053/cys-20-3-2463

Keywords:

Supporting technologies for scientific writing, argumentative zoning, supervised machine learning.

Abstract

SciEsp is a tool for scientific writing in Spanish. Its objective is to help students when writing abstracts of scientific texts, such as a thesis or a dissertation. The tool identifies the different components of an abstract structure according to the guidelines of “good writing” proposed by the literature. Each sentence in the abstract is classified to one of six different rhetorical categories (background, gap, purpose, methodology, result, or conclusion), warning the writer of a possible missing component of the “optimal” structure. We manually annotated a corpus of abstracts from computer science theses and dissertations, and use it to train a Naive Bayes classifier that achieves an F1-measure of 0.65. We expect that SciEsp becomes a starting point for further projects in the area of supporting technologies for scientific writing in Spanish.

Author Biographies

Irvin Vargas-Campos, Pontificia Universidad Católica del Perú

Has a BSc in Computer Engineering from the Pontifical Catholic University of Peru (PUCP), where he also works as a teacher’s assistant and analyst at the Information Technology Department (DTI). His research interests involve using Natural Language Processing and Machine Learning techniques to develop applications that help students write different types of texts properly.

Fernando Alva-Manchego, Pontificia Universidad Católica del Perú

has a Masters degree in Computer Science from the University of Sao Paulo, is a professor at the Pontifical Catholic University of Peru (PUCP) and a member of the Pattern Recognition and Applied Artificial Intelligence Group (GRPIAA). His research interests involve using Natural Language Processing and Machine Learning techniques to develop applications that contribute to the teaching - learning process, as well as to allow information access to people with low literacy levels.

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Published

2016-09-30

Issue

Vol. 20 No. 3 (2016): Thematic Issue: Natural Language Processing and Computational Linguistics (Guest Editor: Alexander Gelbukh)

Section

Articles

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