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Found 48 papers, 2 papers with code
Classification multilabel de concepts médicaux pour l’identification du profil clinique du patient (Multilabel classification of medical concepts for patient’s clinical profile identification )
Nous avons également proposé un modèle « bout-enbout », avec une première phase d’extraction d’entités nommées également basée sur un transformer de type camembert-large et un classifieur de genre sur un modèle Adaboost.
A Benchmark Evaluation of Clinical Named Entity Recognition in French
Objective: This paper presentsan evaluation of masked language models for biomedical French on the task of clinical named entity recognition. Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them tostandard French models CamemBERT, FlauBERT and FrALBERT as well as multilingual mBERT using three publicallyavailable corpora for clinical named entity recognition in French.
Few shot clinical entity recognition in three languages: Masked language models outperform LLM prompting
Results are consistent over the three languages and suggest that few-shot learning using Large language models is not production ready for named entity recognition in the clinical domain.
Impact of translation on biomedical information extraction from real-life clinical notes
The objective of our study is to determine whether using English tools to extract and normalize French medical concepts on translations provides comparable performance to French models trained on a set of annotated French clinical notes.
Detecting automatically the layout of clinical documents to enhance the performances of downstream natural language processing
The precision, recall, and F1 score per document for the acute infection detection algorithm were 82. 54 (95CI 72. 86-91. 60), 85. 24 (95CI 76. 61-93. 70), 83. 87 (95CI 76, 92-90. 08) with exploitation of the results of the advanced body extraction algorithm, respectively.
Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse
The objective of this study is to address the critical issue of de-identification of clinical reports in order to allow access to data for research purposes, while ensuring patient privacy.
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records
We used two French annotated medical datasets to compare our language models to the original CamemBERT network, evaluating the statistical significance of improvement with the Wilcoxon test.
Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021
A cause-effect-tweet dataset was manually labeled and used to train 1) a fine-tuned Bertweet model to detect causal sentences containing a causal association 2) a CRF model with BERT based features to extract possible cause-effect associations.
Effect of depth order on iterative nested named entity recognition models
We provide a set of experiments to study the model's capabilities and the effects of the order on performance.
Participation de l'\'equipe du LIMICS \`a DEFT 2020 (Participation of team LIMICS in the DEFT 2020 challenge )
Nous pr{\'e}sentons dans cet article les m{\'e}thodes con{\c{c}}ues et les r{\'e}sultats obtenus lors de notre participation {\`a} la t{\^a}che 3 de la campagne d{'}{\'e}valuation DEFT 2020, consistant en la reconnaissance d{'}entit{\'e}s nomm{\'e}es du domaine m{\'e}dical.
Mod\`ele neuronal pour la r\'esolution de la cor\'ef\'erence dans les dossiers m\'edicaux \'electroniques (Neural approach for coreference resolution in electronic health records )
La r{\'e}solution de la cor{\'e}f{\'e}rence est un {\'e}l{\'e}ment essentiel pour la constitution automatique de chronologies m{\'e}dicales {\`a} partir des dossiers m{\'e}dicaux {\'e}lectroniques.
Participation de l'\'equipe LAI \`a DEFT 2019 (Participation of team LAI in the DEFT 2019 challenge )
Nous pr{\'e}sentons dans cet article les m{\'e}thodes con{\c{c}}ues et les r{\'e}sultats obtenus lors de notre participation {\`a} la t{\^a}che 3 de la campagne d{'}{\'e}valuation DEFT 2019.
Terminologies augmented recurrent neural network model for clinical named entity recognition
On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69. 5% in exact match, and 84. 1% in non-exact match.
Searching News Articles Using an Event Knowledge Graph Leveraged by Wikidata
News agencies produce thousands of multimedia stories describing events happening in the world that are either scheduled such as sports competitions, political summits and elections, or breaking events such as military conflicts, terrorist attacks, natural disasters, etc.
Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials
Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients.
Evaluation of a Sequence Tagging Tool for Biomedical Texts
Many applications in biomedical natural language processing rely on sequence tagging as an initial step to perform more complex analysis.
Unsupervised Event Clustering and Aggregation from Newswire and Web Articles
In this paper, we present an unsupervised pipeline approach for clustering news articles based on identified event instances in their content.
LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives
In this paper we present our participation to SemEval 2017 Task 12.
Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers
We present a neural architecture for containment relation identification between medical events and/or temporal expressions.
Apprendre des repr\'esentations jointes de mots et d'entit\'es pour la d\'esambigu\"\isation d'entit\'es (Combining Word and Entity Embeddings for Entity Linking)
La d{\'e}sambigu{\"\i}sation d{'}entit{\'e}s (ou liaison d{'}entit{\'e}s), qui consiste {\`a} relier des mentions d{'}entit{\'e}s d{'}un texte {\`a} des entit{\'e}s d{'}une base de connaissance, est un probl{\`e}me qui se pose, entre autre, pour le peuplement automatique de bases de connaissances {\`a} partir de textes.
Temporal information extraction from clinical text
In this paper, we present a method for temporal relation extraction from clinical narratives in French and in English.
Extraction de relations temporelles dans des dossiers \'electroniques patient (Extracting Temporal Relations from Electronic Health Records)
Cette analyse repose sur l{'}extraction d{'}{\'e}v{\'e}nements, d{'}expressions temporelles et des relations entre eux.
A Dataset for Open Event Extraction in English
We detail the methodology used for building the corpus and evaluate some existing systems on this new data.
Datasets for Aspect-Based Sentiment Analysis in French
Aspect Based Sentiment Analysis (ABSA) is the task of mining and summarizing opinions from text about specific entities and their aspects.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
D\'esambigu\"\isation d'entit\'es pour l'induction non supervis\'ee de sch\'emas \'ev\'enementiels
Les pr{\'e}c{\'e}dentes m{\'e}thodes de la litt{\'e}rature utilisent uniquement les t{\^e}tes des syntagmes pour repr{\'e}senter les entit{\'e}s. Pourtant, le groupe complet (par exemple, {''}un homme arm{\'e}{''}) apporte une information plus discriminante (que {''}homme{''}).
Analyse d'expressions temporelles dans les dossiers \'electroniques patients
En s{'}appuyant sur un corpus de documents issus de plusieurs dossiers {\'e}lectroniques patient d{\'e}sidentifi{\'e}s, nous d{\'e}crivons la construction d{'}une ressource annot{\'e}e en expressions temporelles selon la norme TimeML.
Evaluating Web-as-corpus Topical Document Retrieval with an Index of the OpenDirectory
This article introduces a novel protocol and resource to evaluate Web-as-corpus topical document retrieval.
Thematic Cohesion: measuring terms discriminatory power toward themes
This graph can be interpreted as a recommendation graph, where two terms occurring in a same document means that they recommend each other.
Extracting News Web Page Creation Time with DCTFinder
Web pages do not offer reliable metadata concerning their creation date and time.
French Resources for Extraction and Normalization of Temporal Expressions with HeidelTime
French resources have been evaluated in two different ways: on the French TimeBank corpus, a corpus of newspaper articles in French annotated according to the ISO-TimeML standard, and on a user application for automatic building of event timelines.
Evaluating Temporal Graphs Built from Texts via Transitive Reduction
Temporal information has been the focus of recent attention in information extraction, leading to some standardization effort, in particular for the task of relating events in a text.
The Quaero French Medical Corpus: A Ressource for Medical Entity Recognition and Normalization
Overall, a total of 26, 409 entity annotations were mapped to 5, 797 unique UMLS concepts.
A Rough Set Formalization of Quantitative Evaluation with Ambiguity
In this paper, we present the founding elements of a formal model of the evaluation paradigm in natural language processing.
Temporal Annotation: A Proposal for Guidelines and an Experiment with Inter-annotator Agreement
We provide results of an initial application of these guidelines to real news-wire texts in French over several iterations of the annotation process.
WebAnnotator, an Annotation Tool for Web Pages
The HTML rendering fully preserved and all annotations consist in new HTML spans with specific styles.
Event Nominals: Annotation Guidelines and a Manually Annotated Corpus in French
As our application domain is information extraction, we follow a named entity approach to describe and annotate events.
Evolution of Event Designation in Media: Preliminary Study
In this article, we present our methodology concerning the study of the evolution of event designations in French documents from the news agency AFP.
