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.

Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish.

“…The research on negation in French is particularly limited. Aside from a few papers describing rule-based approaches (Deléger and Grouin, 2012;Abdaoui et al, 2017) and the implementation of BiLSTMs by Dalloux et al (2019Dalloux et al ( , 2020, there is barely any other research available on the topic.…”

“…In our experiments we work with a corpus of clinical texts in French (Dalloux et al, 2020), and SFU ReviewSP-NEG, a Spanish corpus of online reviews (Jiménez- Zafra et al, 2018). The English data includes the biological paper abstracts and full scientific articles in the domain of bioinformatics from BioScope , all available subcorpora of the ConanDoyle-neg corpus (Morante and Daelemans, 2012) as well as SFU (SFU Review-NEG, Konstantinova et al, 2012), a large multidomain corpus of product reviews.…”

“…The French data is described in Dalloux et al (2020) and is publicly available on request 3 . It combines two subcorpora of clinical narratives.…”

Processing negation: An introduction to the special issue

“…They apply these criteria to the annotation of scope and focus in the NewsCom corpus, accounting for the first corpus annotated with focus in Spanish. Dalloux et al (2020) present new corpora for Brazilian Portuguese and French manually annotated with negation cues and their scopes in clinical documents. They also present automatic methods based on supervised machine learning approaches for the automatic detection of negation cues and their scopes, namely vector representations and neural networks.…”

“…These six articles can be grouped based on two criteria: the languages they work with and whether they process negation in order to improve some application. Three articles work with English texts (Schulder, Wiegand, and Ruppenhofer 2020;Barnes, Velldal, and Øvrelid 2020;Sykes et al 2020), and three articles work with texts in other languages: Spanish (Jiménez-Zafra et al 2020;Taul et al 2020), and French and Brazilian Portuguese (Dalloux et al 2020). Regarding applications, three articles present work on processing negation for sentiment analysis (Jiménez-Zafra et al 2020; Schulder et al 2020; Barnes et al 2020), two work in the biomedical domain (Dalloux et al 2020;Sykes et al 2020), and one presents a corpus with focus on negation annotations (Taul et al 2020).…”