Mouhamadou Ba


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Handling Entity Normalization with no Annotated Corpus: Weakly Supervised Methods Based on Distributional Representation and Ontological Information
Arnaud Ferré | Robert Bossy | Mouhamadou Ba | Louise Deléger | Thomas Lavergne | Pierre Zweigenbaum | Claire Nédellec
Proceedings of the Twelfth Language Resources and Evaluation Conference

Entity normalization (or entity linking) is an important subtask of information extraction that links entity mentions in text to categories or concepts in a reference vocabulary. Machine learning based normalization methods have good adaptability as long as they have enough training data per reference with a sufficient quality. Distributional representations are commonly used because of their capacity to handle different expressions with similar meanings. However, in specific technical and scientific domains, the small amount of training data and the relatively small size of specialized corpora remain major challenges. Recently, the machine learning-based CONTES method has addressed these challenges for reference vocabularies that are ontologies, as is often the case in life sciences and biomedical domains. And yet, its performance is dependent on manually annotated corpus. Furthermore, like other machine learning based methods, parametrization remains tricky. We propose a new approach to address the scarcity of training data that extends the CONTES method by corpus selection, pre-processing and weak supervision strategies, which can yield high-performance results without any manually annotated examples. We also study which hyperparameters are most influential, with sometimes different patterns compared to previous work. The results show that our approach significantly improves accuracy and outperforms previous state-of-the-art algorithms.


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Bacteria Biotope at BioNLP Open Shared Tasks 2019
Robert Bossy | Louise Deléger | Estelle Chaix | Mouhamadou Ba | Claire Nédellec
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

This paper presents the fourth edition of the Bacteria Biotope task at BioNLP Open Shared Tasks 2019. The task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts, and the characterization of these entities with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on biodiversity for fundamental research and applications in microbiology. The paper describes the different proposed subtasks, the corpus characteristics, and the challenge organization. We also provide an analysis of the results obtained by participants, and inspect the evolution of the results since the last edition in 2016.


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Overview of the Regulatory Network of Plant Seed Development (SeeDev) Task at the BioNLP Shared Task 2016.
Estelle Chaix | Bertrand Dubreucq | Abdelhak Fatihi | Dialekti Valsamou | Robert Bossy | Mouhamadou Ba | Louise Deléger | Pierre Zweigenbaum | Philippe Bessières | Loic Lepiniec | Claire Nédellec
Proceedings of the 4th BioNLP Shared Task Workshop

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Overview of the Bacteria Biotope Task at BioNLP Shared Task 2016
Louise Deléger | Robert Bossy | Estelle Chaix | Mouhamadou Ba | Arnaud Ferré | Philippe Bessières | Claire Nédellec
Proceedings of the 4th BioNLP Shared Task Workshop