Comparing Supervised Machine Learning Techniques for Genre Analysis in Software Engineering Research Articles
| Autor: | Felipe Araújo de Britto, Fernando Silva Parreiras, Leonardo Pereira Nunes, Thiago Castro Ferreira |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science media_common.quotation_subject Context (language use) computer.software_genre Logistic regression Machine learning Support vector machine Rhetorical question Genre analysis Quality (business) Artificial intelligence Software engineering business computer Natural language processing media_common |
| Zdroj: | RANLP |
| DOI: | 10.26615/978-954-452-072-4_008 |
| Popis: | Written communication is of utmost importance to the progress of scientific research. The speed of such development, however, may be affected by the scarcity of reviewers to referee the quality of research articles. In this context, automatic approaches that are able to query linguistic segments in written contributions by detecting the presence or absence of common rhetorical patterns have become a necessity. This paper aims to compare supervised machine learning techniques tested to accomplish genre analysis in Introduction sections of software engineering articles. A semi-supervised approach was carried out to augment the number of annotated sentences in SciSents (Avaliable on: ANONYMOUS). Two supervised approaches using SVM and logistic regression were undertaken to assess the F-score for genre analysis in the corpus. A technique based on logistic regression and BERT has been found to perform genre analysis highly satisfactorily with an average of 88.25 on F-score when retrieving patterns at an overall level. |
| Databáze: | OpenAIRE |
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