Paper Info
Title
Supervised Learning For The Detection Of Negation And Of Its Scope In French And Brazilian Portuguese Biomedical Corpora
Abstract
Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.
Year
DOI
Venue
2021
10.1017/S1351324920000352
NATURAL LANGUAGE ENGINEERING
Keywords
DocType
Volume
Corpus annotation, Machine learning, Natural language processing for biomedical texts, Information extraction
Journal
27
Issue
ISSN
Citations 
2
1351-3249
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Clément Dalloux110.35
Vincent Claveau216232.15
Natalia Grabar397.64
Lucas E. Silva e Oliveira433.06
Claudia M. C. Moro553.52
Yohan Bonescki Gumiel621.71
Deborah Ribeiro Carvalho710.35