Papers by Claire Nédellec
Bacteria Biotope at BioNLP Open Shared Tasks 2019 (D19-57)
Copied to clipboard
| Challenge: | The Bacteria Biotope task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts. |
| Approach: | They propose to use PubMed abstracts and full-text excerpts to extract the locations and phenotypes of microorganisms and to characterizations of these entities with respect to reference knowledge sources. |
| Outcome: | The proposed subtasks, the corpus characteristics, and the challenge organization are compared with the previous edition in 2016 and the results are presented in the second edition. |
SCoNE: a Self-Correcting and Noise-Augmented Method for Complex Biological and Chemical Named Entity Recognition (2026.eacl-long)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a fundamental task aimed at identifying entities such as names, locations, and organizations. |
| Approach: | They propose a self-correcting and noise-augmented method for complex Biological and Chemical Named Entity Recognition that improves learning diversity and confidence. |
| Outcome: | The proposed method outperforms baseline methods on CHEMDNER and microbial ecology datasets by 1.80 and 2.73 F1-scores. |
Handling Entity Normalization with no Annotated Corpus: Weakly Supervised Methods Based on Distributional Representation and Ontological Information (2020.lrec-1)
Copied to clipboard
Arnaud Ferré, Robert Bossy, Mouhamadou Ba, Louise Deléger, Thomas Lavergne, Pierre Zweigenbaum, Claire Nédellec
| Challenge: | Entity normalization is an important subtask of information extraction . it links entities mentions in text to categories or concepts in a reference vocabulary . |
| Approach: | They propose a method that uses corpus selection, pre-processing and weak supervision strategies to address the scarcity of training data. |
| Outcome: | The proposed method outperforms state-of-the-art methods in terms of accuracy and parametrization . it uses corpus selection, pre-processing and weak supervision strategies . |
Combining rule-based and embedding-based approaches to normalize textual entities with an ontology (L18-1)
Copied to clipboard
| Challenge: | a method to normalize multi-word terms with concepts from a domain-specific ontology is proposed . a large part of knowledge is expressed in textual form, such as in scientific articles . |
| Approach: | They propose a method to normalize multi-word terms with concepts from a domain-specific ontology. |
| Outcome: | The proposed method outperforms existing methods on a categorization task in bacterial habitats . the results are encouraging, and the proposed method is expected to be widely used in the biomedical/biological field . |