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Found 13 papers, 0 papers with code
Explanatory Argument Extraction of Correct Answers in Resident Medical Exams
Comprehensive experimentation with language models for Spanish shows that sometimes multilingual models fare better than monolingual ones, even outperforming models which have been adapted to the medical domain.
EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing
We demonstrate that EriBERTa outperforms previous Spanish language models in the clinical domain, showcasing its superior capabilities in understanding medical texts and extracting meaningful information.
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine
Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task.
IxaMed at PharmacoNER Challenge 2019
In order to identify named entities we have made use of a Bi-LSTM with a CRF on top in combination with different types of word embeddings.
Towards discourse annotation and sentiment analysis of the Basque Opinion Corpus
Discourse information is crucial for a better understanding of the text structure and it is also necessary to describe which part of an opinionated text is more relevant or to decide how a text span can change the polarity (strengthen or weaken) of other span by means of coherence relations.
Saying no but meaning yes: negation and sentiment analysis in Basque
In this work, we have analyzed the effects of negation on the semantic orientation in Basque.
The impact of simple feature engineering in multilingual medical NER
The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER.
Fully unsupervised low-dimensional representation of adverse drug reaction events through distributional semantics
In brief, the ADRs are represented as vectors that link the drug with the disease in their context through a recursive additive model.
