Search Results for author: Natalia Grabar

Found 47 papers, 2 papers with code

Identification of complex words and passages in medical documents in French

no code implementations JEP/TALN/RECITAL 2022 Kim Cheng SHEANG, Anaïs Koptient, Natalia Grabar, Horacio Saggion

Nous proposons de travail sur l’identification de mots et passages complexes dans les documents biomédicaux en français.

Automatic Detection of Difficulty of French Medical Sequences in Context

no code implementations LREC (MWE) 2022 Anaïs Koptient, Natalia Grabar

Before the simplification step of such terms, it is important to detect difficult to understand syntactic groups in medical documents as they may correspond to or contain technical terms.

Simplification automatique de textes biomédicaux en français: lorsque des données précises de petite taille aident (French Biomedical Text Simplification : When Small and Precise Helps )

no code implementations JEP/TALN/RECITAL 2021 Remi Cardon, Natalia Grabar

Nous présentons un résumé en français et un résumé en anglais de l’article (Cardon & Grabar, 2020), publié dans les actes de la conférence 28th International Conference on Computational Linguistics (COLING 2020).

Text Simplification

DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain

1 code implementation20 Feb 2024 Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier, Pacome Constant dit Beaufils, Natalia Grabar, Beatrice Daille, Solen Quiniou, Emmanuel Morin, Pierre-Antoine Gourraud, Richard Dufour

This limitation hampers the evaluation of the latest French biomedical models, as they are either assessed on a minimal number of tasks with non-standardized protocols or evaluated using general downstream tasks.

named-entity-recognition Named Entity Recognition +3

Pr\'edire le niveau de langue d'apprenants d'anglais (Predict the language level for English learners)

no code implementations JEPTALNRECITAL 2020 Natalia Grabar, Thierry Hamon, Bert Cappelle, Gr, Cyril in, Beno{\^\i}t Leclercq, Ilse Depraetere

L{'}analyse de productions d{'}apprenants int{\'e}resse les chercheurs et les enseignants car cela permet d{'}avoir une meilleure id{\'e}e des difficult{\'e}s et les facilit{\'e}s d{'}apprentissage et de faire des programmes didactiques plus adapt{\'e}s. Cela peut {\'e}galement donner des indications sur les difficult{\'e}s cognitives {\`a} ma{\^\i}triser les notions grammaticales abstraites dans une nouvelle langue.

Reducing the Search Space for Parallel Sentences in Comparable Corpora

no code implementations LREC 2020 R{\'e}mi Cardon, Natalia Grabar

This paper describes and evaluates simple techniques for reducing the research space for parallel sentences in monolingual comparable corpora.

Sentence

Speculation and Negation detection in French biomedical corpora

no code implementations RANLP 2019 Cl{\'e}ment Dalloux, Vincent Claveau, Natalia Grabar

We reach up to 97. 21 {\%} and 91. 30 {\%} F-measure for the detection of negation and speculation cues, respectively, using CRFs.

Negation Negation Detection +1

Clinical Case Reports for NLP

no code implementations WS 2019 Cyril Grouin, Natalia Grabar, Vincent Claveau, Thierry Hamon

Thus, we manually annotated a set of 717 files into four general categories (age, gender, outcome, and origin) for a total number of 2, 835 annotations.

Simplification-induced transformations: typology and some characteristics

no code implementations WS 2019 Ana{\"\i}s Koptient, R{\'e}mi Cardon, Natalia Grabar

The purpose of automatic text simplification is to transform technical or difficult to understand texts into a more friendly version.

Text Simplification

RNN Embeddings for Identifying Difficult to Understand Medical Words

1 code implementation WS 2019 Hanna Pylieva, Artem Chernodub, Natalia Grabar, Thierry Hamon

We introduce novel embeddings received from RNN - FrnnMUTE (French RNN Medical Understandability Text Embeddings) which allow to reach up to 87. 0 F1 score in identification of difficult words.

Word Embeddings

Corpus annot\'e de cas cliniques en fran\ccais (Annotated corpus with clinical cases in French)

no code implementations JEPTALNRECITAL 2019 Natalia Grabar, Cyril Grouin, Thierry Hamon, Vincent Claveau

Pour r{\'e}pondre {\`a} ce d{\'e}fi, nous pr{\'e}sentons dans cet article le corpus CAS contenant des cas cliniques de patients, r{\'e}els ou fictifs, que nous avons compil{\'e}s. Ces cas cliniques en fran{\c{c}}ais couvrent plusieurs sp{\'e}cialit{\'e}s m{\'e}dicales et focalisent donc sur diff{\'e}rentes situations cliniques.

CAS: French Corpus with Clinical Cases

no code implementations WS 2018 Natalia Grabar, Vincent Claveau, Cl{\'e}ment Dalloux

Textual corpora are extremely important for various NLP applications as they provide information necessary for creating, setting and testing these applications and the corresponding tools.

Information Retrieval

Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers

no code implementations WS 2018 R{\'e}mi Cardon, Natalia Grabar

We propose to exploit comparable corpora in French which are distinguished by their registers (specialized and simplified versions) to detect and align parallel sentences.

Information Retrieval Machine Translation +2

D\'etection de m\'esusages de m\'edicaments dans les r\'eseaux sociaux (Detection of drug misuse in social media)

no code implementations JEPTALNRECITAL 2018 Elise Bigeard, Natalia Grabar, Frantz Thiessard

L{'}objectif de notre travail consiste {\`a} explorer les forums de sant{\'e} avec des m{\'e}thodes de classification supervis{\'e}e afin d{'}identifier les messages contenant un m{\'e}susage de m{\'e}dicament.

POMELO: Medline corpus with manually annotated food-drug interactions

no code implementations RANLP 2017 Thierry Hamon, Vincent Tabanou, Fleur Mougin, Natalia Grabar, Frantz Thiessard

When patients take more than one medication, they may be at risk of drug interactions, which means that a given drug can cause unexpected effects when taken in combination with other drugs.

Understanding of unknown medical words

no code implementations RANLP 2017 Natalia Grabar, Thierry Hamon

Then, two kinds of analysis are performed: analysis of the evolution of understandable and non-understandable words (globally and according to some suffixes) and analysis of clusters created with unsupervised algorithms on basis of linguistic and extra-linguistic features of the studied words.

Crit\`eres num\'eriques dans les essais cliniques : annotation, d\'etection et normalisation (Numerical criteria in clinical trials : annotation, detection and normalization)

no code implementations JEPTALNRECITAL 2017 Natalia Grabar, Vincent Claveau

Les essais cliniques sont un {\'e}l{\'e}ment fondamental pour l{'}{\'e}valuation de nouvelles th{\'e}rapies ou techniques de diagnostic, de leur s{\'e}curit{\'e} et efficacit{\'e}.

Analyse et \'evolution de la compr\'ehension de termes techniques (Analysis and Evolution of Understanding of Technical Terms)

no code implementations JEPTALNRECITAL 2017 Natalia Grabar, Thierry Hamon

Nous faisons l{'}hypoth{\`e}se que les mots techniques inconnus dot{\'e}s d{'}une structure interne (mots affix{\'e}s ou compos{\'e}s) peuvent fournir des indices linguistiques {\`a} un locuteur, ce qui peut l{'}aider {\`a} analyser et {\`a} comprendre ces mots.

Vers une analyse des diff\'erences interlinguistiques entre les genres textuels : \'etude de cas bas\'ee sur les n-grammes et l'analyse factorielle des correspondances (Towards a cross-linguistic analysis of genres: A case study based on n-grams and Correspondence Analysis)

no code implementations JEPTALNRECITAL 2016 Marie-Aude Lefer, Yves Bestgen, Natalia Grabar

Ensuite, pour chaque longueur, les 1 000 n-grammes les plus fr{\'e}quents dans chaque langue sont trait{\'e}s par l{'}AFC pour d{\'e}terminer quels n-grammes sont particuli{\`e}rement saillants dans les genres {\'e}tudi{\'e}s. Enfin, les n-grammes sont cat{\'e}goris{\'e}s manuellement en distinguant les expressions d{'}opinion et de certitude, les marqueurs discursifs et les expressions r{\'e}f{\'e}rentielles.

Pr\'ediction automatique de fonctions pragmatiques dans les reformulations (Automatic prediction of pragmatic functions in reformulations)

no code implementations JEPTALNRECITAL 2016 Natalia Grabar, Iris Eshkol-Taravella

Les donn{\'e}es de r{\'e}f{\'e}rence sont issues d{'}annotations manuelles et consensuelles des reformulations spontan{\'e}es form{\'e}es autour de trois marqueurs (c{'}est-{\`a}-dire, je veux dire, disons).

A Large Rated Lexicon with French Medical Words

no code implementations LREC 2016 Natalia Grabar, Thierry Hamon

The purpose of our work is to build specific lexicon in which the words are rated according to whether they are understandable or non-understandable.

Detection of Reformulations in Spoken French

no code implementations LREC 2016 Natalia Grabar, Iris Eshkol-Taravela

Detection of enunciations with reformulations shows up to 0. 66 precision.

...des conf\'erences enfin disons des causeries... D\'etection automatique de segments en relation de paraphrase dans les reformulations de corpus oraux

no code implementations JEPTALNRECITAL 2015 Natalia Grabar, Iris Eshkol

Une m{\'e}thode automatique fond{\'e}e sur l{'}apprentissage avec les CRF est propos{\'e}e afin de d{\'e}tecter les segments paraphras{\'e}s. Diff{\'e}rents descripteurs sont exploit{\'e}s dans une fen{\^e}tre de taille variable.

Relation

Extraction automatique de paraphrases grand public pour les termes m\'edicaux

no code implementations JEPTALNRECITAL 2015 Natalia Grabar, Thierry Hamon

La m{\'e}thode est bas{\'e}e sur l{'}analyse morphologique des termes, l{'}analyse syntaxique et la fouille de textes non sp{\'e}cialis{\'e}s. L{'}analyse et l{'}{\'e}valuation des r{\'e}sultats indiquent que de telles paraphrases peuvent {\^e}tre trouv{\'e}es dans les documents non sp{\'e}cialis{\'e}s et pr{\'e}sentent une compr{\'e}hension plus facile.

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