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Contextualized French Language Models for Biomedical Named Entity Recognition

Jenny Copara, Julien Knafou, Nona Naderi, Claudia Moro, Patrick Ruch, Douglas Teodoro
2020 Traitement Automatique des Langues Naturelles & Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues  
Modèles contextualisés en langue française pour la reconnaissance des entités nommées dans le domaine biomédical La reconnaissance des entités nommées (NER) est essentielle pour les applications biomédicales  ...  L'utilisation d'un ensemble de modèles de langages neuronaux s'est révélée très efficace, améliorant une base de référence du CRF de 28% et un modèle de langage spécialisé unique de 4%.  ...  Named entity recognition (NER) is a key part in information extraction systems.  ... 
dblp:conf/taln/CoparaKNMRT20 fatcat:e2mex7el2fdllbukksvu7wnmtq

Editorial for the Special Issue on "Natural Language Processing and Text Mining"

Pablo Gamallo, Marcos Garcia
2019 Information  
Natural language processing (NLP) and Text Mining (TM) are a set of overlapping strategies working on unstructured text [...]  ...  In the paper "Transfer Learning for Named Entity Recognition in Financial and Biomedical Documents" [1] , the authors deal with the problem of applying named entity recognition (NER) models on different  ...  The paper "An Improved Word Representation for Deep Learning Based NER in Indian Languages" [9] describes a named entity recognition system based on deep learning approaches for Indian languages.  ... 
doi:10.3390/info10090279 fatcat:mqgmakagw5gjthh2dcztn72b4e

TaxoNERD: deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature [article]

Nicolas Le Guillarme, Wilfried Thuiller
2021 bioRxiv   pre-print
A prerequisite is the ability to recognise mentions of taxa in text, a special case of named entity recognition (NER).  ...  One promising direction is to leverage the huge corpus of unlabelled ecological texts to learn a language representation model that could benefit downstream tasks.  ...  for named entity recognition, part-of-speech tagging, dependency parsing, text classification F I G U R E 2 spaCy's generic neural architecture for named entity recognition. and more. spaCy's models have  ... 
doi:10.1101/2021.06.08.444426 fatcat:l2epu7suznc4taiknapvf73j44

Effect of depth order on iterative nested named entity recognition models [article]

Perceval Wajsburt, Yoann Taillé, Xavier Tannier
2021 arXiv   pre-print
This paper studies the effect of the order of depth of mention on nested named entity recognition (NER) models.  ...  Thus, iterative models for nested NER use multiple predictions to enumerate all entities, imposing a predefined order from largest to smallest or smallest to largest.  ...  To our knowledge, our work is the first to evaluate a nested biomedical concept recognition system in the French language, which constitutes an additional contribution, as resources for languages other  ... 
arXiv:2104.01037v1 fatcat:l4ntaaqbafd3vhikgbrp3x57qa

EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts [article]

Irene Li, Keen You, Xiangru Tang, Yujie Qiao, Lucas Huang, Chia-Chun Hsieh, Benjamin Rosand, Jeremy Goldwasser, Dragomir Radev
2022 arXiv   pre-print
The second part integrates many third-party libraries for up to 12 off-shelf NLP tasks such as named entity recognition, summarization, machine translation, etc.  ...  The success of the recent neural Natural Language Processing (NLP) method has led to a new direction for processing unstructured clinical notes.  ...  It supports multiple methods for tokenization, part of speech tagging, dependency parsing, and named entity recognition. Lang.  ... 
arXiv:2204.06604v4 fatcat:xdnykxeb3ngctg2tajomg3pp7m

Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora

Nona Naderi, Julien Knafou, Jenny Copara, Patrick Ruch, Douglas Teodoro
2021 Frontiers in Research Metrics and Analytics  
To unlock the value of such corpora, named entity recognition (NER) methods are proposed.  ...  The health and life science domains are well known for their wealth of named entities found in large free text corpora, such as scientific literature and electronic health records.  ...  ACKNOWLEDGMENTS The authors would like to thank the reviewers for their valuable comments and suggestions.  ... 
doi:10.3389/frma.2021.689803 pmid:34870074 pmcid:PMC8640190 fatcat:s363qdk4gvaixnve3ixm2dzppq

Cross-lingual Unified Medical Language System entity linking in online health communities

Yonatan Bitton, Raphael Cohen, Tamar Schifter, Eitan Bachmat, Michael Elhadad, Noémie Elhadad
2020 JAMIA Journal of the American Medical Informatics Association  
(2) an unsupervised method for creating a transliteration dataset in any language without manually labeled data, and (3) an efficient way to identify and link medical entities in the Hebrew corpus to  ...  To reduce their variability, medical terms must be normalized, such as linking them to Unified Medical Language System (UMLS) concepts.  ...  Several CLEF eHealth challenges (2015-2019) have focused on named-entity recognition in English and French in biomedical articles, with applications to multilingual information extraction from health reports  ... 
doi:10.1093/jamia/ocaa150 pmid:32910823 fatcat:pjnexvgv5rcydfpgdljfrzbequ

A Year of Papers Using Biomedical Texts:

Cyril Grouin, Natalia Grabar, Section Editors for the IMIA Yearbook Section on Natural Language Processing
2020 IMIA Yearbook of Medical Informatics  
Objectives: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field.  ...  [4] performed a named entity recognition using several models trained through BiLSTM, while Si et al. [17] produced contextual embeddings to improve their concept extraction method.  ...  discharge summaries [10] , and more rarely triage notes [11] for performing named entity recognition.  ... 
doi:10.1055/s-0040-1701997 pmid:32823319 fatcat:gfkpot4xmffuvh7uxp3rp62neq

Survey of NLP in Pharmacology: Methodology, Tasks, Resources, Knowledge, and Tools [article]

Dimitar Trajanov, Vangel Trajkovski, Makedonka Dimitrieva, Jovana Dobreva, Milos Jovanovik, Matej Klemen, Aleš Žagar, Marko Robnik-Šikonja
2022 arXiv   pre-print
As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology.  ...  Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications  ...  Acknowledgments This work is also based on COST Action CA18209 -NexusLinguarum "European network for Web-centred linguistic data science", supported by COST (European Cooperation in Science and Technology  ... 
arXiv:2208.10228v1 fatcat:76kwzhx53fbfpowwjie5neeicu

Pre-trained Language Models in Biomedical Domain: A Systematic Survey [article]

Benyou Wang, Qianqian Xie, Jiahuan Pei, Prayag Tiwari, Zhao Li, Jie fu
2021 arXiv   pre-print
Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks.  ...  In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.  ...  full-text articles) for biomedical named entity recognition.  ... 
arXiv:2110.05006v2 fatcat:aykwfhgi4jgmfovissgdvknny4

Clinical Natural Language Processing in languages other than English: opportunities and challenges

Aurélie Névéol, Hercules Dalianis, Sumithra Velupillai, Guergana Savova, Pierre Zweigenbaum
2018 Journal of Biomedical Semantics  
This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English.  ...  other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation  ...  recognition, normalization and contextualization.  ... 
doi:10.1186/s13326-018-0179-8 pmid:29602312 pmcid:PMC5877394 fatcat:xas3ynaltjeuhgdymutha7akvi

SIFR annotator: ontology-based semantic annotation of French biomedical text and clinical notes

Andon Tchechmedjiev, Amine Abdaoui, Vincent Emonet, Stella Zevio, Clement Jonquet
2018 BMC Bioinformatics  
Despite a wide adoption of English in science, a significant amount of biomedical data are produced in other languages, such as French.  ...  Additionally, the SIFR Annotator is the first openly web accessible tool to annotate and contextualize French biomedical text with ontology concepts leveraging a dictionary currently made of 28 terminologies  ...  Funding This work was funded by the Semantic Indexing of French biomedical Resources ( and PractiKPharma projects (http:// that received funding from the French  ... 
doi:10.1186/s12859-018-2429-2 pmid:30400805 pmcid:PMC6218966 fatcat:dmwmnr4qs5fbfgcz2obqualfpq

Semantic Tagging and Normalization of French Medical Entities

Jorge Vivaldi, Horacio Rodríguez, Viviana Cotik
2016 Conference and Labs of the Evaluation Forum  
The first one is a semantic tagger aiming to detect relevant entities in French medical documents, tagging them with their appropriate semantic class and normalizing them with the Semantic Groups codes  ...  In this paper we present two tools for facing task 2 in CLEF eHealth 2016.  ...  Related Work English is, by far, the most supported language for biomedical resources and tools.  ... 
dblp:conf/clef/VivaldiRC16 fatcat:yc4culm5bzey7hcagdwqyvxoh4

The Impact of Specialized Corpora for Word Embeddings in Natural Langage Understanding

Antoine Neuraz, Bastien Rance, Nicolas Garcelon, Leonardo Campillos Llanos, Anita Burgun, Sophie Rosset
2020 Studies in Health Technology and Informatics  
Recent studies in the biomedical domain suggest that learning statistical word representations (static or contextualized word embeddings) on large corpora of specialized data improve the results on downstream  ...  natural language processing (NLP) tasks.  ...  [9] evaluated ELMo embeddings on named entity recognition tasks in the biomedical domain.  ... 
doi:10.3233/shti200197 pmid:32570421 fatcat:ce2xhgfcxrgane5muz324gbctm

Semantic Tagging of French Medical Entities Using Distant Learning

Viviana Cotik, Jorge Vivaldi, Horacio Rodríguez
2015 Conference and Labs of the Evaluation Forum  
In this paper we present a semantic tagger aiming to detect relevant entities in French medical documents and tagging them with their appropriate semantic class.  ...  The system presented uses a set of binary classifiers, and a combination mechanisms for combining the results of the classifiers.  ...  The first one is similar to term detection and Named Entity Recognition (NER), while the latter is closely related to Named Entity Classification (NEC ).  ... 
dblp:conf/clef/CotikVR15 fatcat:7spmb4eidnaydaw2xqro6jog3q
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