Zobrazeno 1 - 10
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pro vyhledávání: '"Zweigenbaum, P."'
This paper describes our submission to Task 2 of SemEval-2024: Safe Biomedical Natural Language Inference for Clinical Trials. The Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT) consists of a Textual Entailment (TE) task f
Externí odkaz:
http://arxiv.org/abs/2404.03977
Autor:
Raithel, Lisa, Yeh, Hui-Syuan, Yada, Shuntaro, Grouin, Cyril, Lavergne, Thomas, Névéol, Aurélie, Paroubek, Patrick, Thomas, Philippe, Nishiyama, Tomohiro, Möller, Sebastian, Aramaki, Eiji, Matsumoto, Yuji, Roller, Roland, Zweigenbaum, Pierre
User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific
Externí odkaz:
http://arxiv.org/abs/2403.18336
Autor:
Raithel, Lisa, Thomas, Philippe, Roller, Roland, Sapina, Oliver, Möller, Sebastian, Zweigenbaum, Pierre
In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get a
Externí odkaz:
http://arxiv.org/abs/2208.02031
Relation extraction is a core problem for natural language processing in the biomedical domain. Recent research on relation extraction showed that prompt-based learning improves the performance on both fine-tuning on full training set and few-shot tr
Externí odkaz:
http://arxiv.org/abs/2204.10360
Publikováno v:
Junior Conference on Data Science and Engineering, Feb 2021, Orsay, France
We investigate a method to extract relations from texts based on global alignment and syntactic information. Combined with SVM, this method is shown to have a performance comparable or even better than LSTM on two RE tasks.
Externí odkaz:
http://arxiv.org/abs/2112.02097
Publikováno v:
BioCreative VII Challenge Evaluation Workshop, Nov 2021, on-line, Spain
Recently many studies have been conducted on the topic of relation extraction. The DrugProt track at BioCreative VII provides a manually-annotated corpus for the purpose of the development and evaluation of relation extraction systems, in which inter
Externí odkaz:
http://arxiv.org/abs/2112.02955
Autor:
Boukkouri, Hicham El, Ferret, Olivier, Lavergne, Thomas, Noji, Hiroshi, Zweigenbaum, Pierre, Tsujii, Junichi
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 link
Externí odkaz:
http://arxiv.org/abs/2010.10392
Recent work in cross-lingual contextual word embedding learning cannot handle multi-sense words well. In this work, we explore the characteristics of contextual word embeddings and show the link between contextual word embeddings and word senses. We
Externí odkaz:
http://arxiv.org/abs/1909.08681
Publikováno v:
BMC Bioinformatics, Vol 21, Iss S23, Pp 1-19 (2020)
Abstract Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specializ
Externí odkaz:
https://doaj.org/article/88f113a8208d4915abbd31ee9b1d0d68
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 20, Iss S3, Pp 1-11 (2020)
Abstract Background It is of utmost importance to investigate novel therapies for cancer, as it is a major cause of death. In recent years, immunotherapies, especially those against immune checkpoints, have been developed and brought significant impr
Externí odkaz:
https://doaj.org/article/0f4a6bf34bc44f5b83a14a14e830021c