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Found 35 papers, 4 papers with code
Re-train or Train from Scratch? Comparing Pre-training Strategies of BERT in the Medical Domain
BERT models used in specialized domains all seem to be the result of a simple strategy: initializing with the original BERT and then resuming pre-training on a specialized corpus.
Two-Step MT: Predicting Target Morphology
This paper describes a two-step machine translation system that addresses the issue of translating into a morphologically rich language (English to Czech), by performing separately the translation and the generation of target morphology.
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world.
Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction
In this paper, we investigate prompting for biomedical relation extraction, with experiments on the ChemProt dataset.
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite it not being intrinsically linked to the notion of Transformers.
Ranked #1 on
Semantic Similarity
on ClinicalSTS
Clinical Concept Extraction
Drug–drug Interaction Extraction
+3
Handling Entity Normalization with no Annotated Corpus: Weakly Supervised Methods Based on Distributional Representation and Ontological Information
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.
Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition
Using pre-trained word embeddings in conjunction with Deep Learning models has become the {``}de facto{''} approach in Natural Language Processing (NLP).
Ranked #4 on
Clinical Concept Extraction
on 2010 i2b2/VA
DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation
We provide a preliminary analysis of the corpus to confirm that the participants' judgments reveal perceptible differences in MT quality between the two MT systems used.
Detecting context-dependent sentences in parallel corpora
In this article, we provide several approaches to the automatic identification of parallel sentences that require sentence-external linguistic context to be correctly translated.
Learning the Structure of Variable-Order CRFs: a finite-state perspective
The computational complexity of linear-chain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies.
D\'etection de concepts et granularit\'e de l'annotation (Concept detection and annotation granularity )
Nous faisons l{'}hypoth{\`e}se qu{'}une annotation {\`a} un niveau de granularit{\'e} plus fin, typiquement au niveau de l{'}{\'e}nonc{\'e}, devrait am{\'e}liorer la performance d{'}un d{\'e}tecteur automatique entra{\^\i}n{\'e} sur ces donn{\'e}es.
Traitement automatique de la langue biom\'edicale au LIMSI (Biomedical language processing at LIMSI)
Nous proposons des d{\'e}monstrations de trois outils d{\'e}velopp{\'e}s par le LIMSI en traitement automatique des langues appliqu{\'e} au domaine biom{\'e}dical : la d{\'e}tection de concepts m{\'e}dicaux dans des textes courts, la cat{\'e}gorisation d{'}articles scientifiques pour l{'}assistance {\`a} l{'}{\'e}criture de revues syst{\'e}matiques, et l{'}anonymisation de textes cliniques.
A Dataset for ICD-10 Coding of Death Certificates: Creation and Usage
Very few datasets have been released for the evaluation of diagnosis coding with the International Classification of Diseases, and only one so far in a language other than English.
Supervised classification of end-of-lines in clinical text with no manual annotation
In some plain text documents, end-of-line marks may or may not mark the boundary of a text unit (e. g., of a paragraph).
Une cat\'egorisation de fins de lignes non-supervis\'ee (End-of-line classification with no supervision)
Nous proposons une m{\'e}thode enti{\`e}rement non-supervis{\'e}e pour d{\'e}terminer si une fin de ligne doit {\^e}tre vue comme un simple espace ou comme une v{\'e}ritable fronti{\`e}re d{'}unit{\'e} textuelle, et la testons sur un corpus de comptes rendus m{\'e}dicaux.
Oublier ce qu'on sait, pour mieux apprendre ce qu'on ne sait pas : une \'etude sur les contraintes de type dans les mod\`eles CRF
Quand on dispose de connaissances a priori sur les sorties possibles d{'}un probl{\`e}me d{'}{\'e}tiquetage, il semble souhaitable d{'}inclure cette information lors de l{'}apprentissage pour simplifier la t{\^a}che de mod{\'e}lisation et acc{\'e}l{\'e}rer les traitements.
Etiquetage morpho-syntaxique en domaine de sp\'ecialit\'e: le domaine m\'edical
En perspective de ce travail, nous envisageons une application du corpus clinique annot{\'e} pour am{\'e}liorer l{'}{\'e}tiquetage morpho-syntaxique des documents cliniques en fran{\c{c}}ais.
Automatic language identity tagging on word and sentence-level in multilingual text sources: a case-study on Luxembourgish
This language identification system was used to select textual data extracted from the web, in order to build a lexicon and language models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Joint Segmentation and POS Tagging for Arabic Using a CRF-based Classifier
Arabic is a morphologically rich language, and Arabic texts abound of complex word forms built by concatenation of multiple subparts, corresponding for instance to prepositions, articles, roots prefixes, or suffixes.
