scholarly journals Applications for Flexible TFT Arrays Emerge in the Biomedical Domain

2021 ◽  
Vol 37 (4) ◽  
pp. 26-33
Author(s):  
Auke Jisk Kronemeijer ◽  
Gerwin H. Gelinck
Keyword(s):  
Biomedical Domain

scholarly journals Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Clément Dalloux ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Lucas Emanuel Silva Oliveira ◽  
Claudia Maria Cabral Moro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
State Of The Art ◽  
Automatic Detection ◽  
Brazilian Portuguese ◽  
Supervised Machine Learning ◽  
Biomedical Domain ◽  
Learning Approaches ◽  
Cross Domain ◽  
Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

Word Sense Disambiguation in the Biomedical Domain: An Overview

2005 ◽  
Vol 12 (5) ◽  
pp. 554-565 ◽  
Author(s):  
Martijn J. Schuemie ◽  
Jan A. Kors ◽  
Barend Mons
Keyword(s):  
Word Sense Disambiguation ◽  
Biomedical Domain ◽  
Word Sense ◽  
Sense Disambiguation

scholarly journals BioCause: Annotating and analysing causality in the biomedical domain

2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Claudiu Mihăilă ◽  
Tomoko Ohta ◽  
Sampo Pyysalo ◽  
Sophia Ananiadou
Keyword(s):  
Biomedical Domain

Microlens array snapshot hyperspectral microscopy system for the biomedical domain

2021 ◽  
Vol 60 (7) ◽  
pp. 1896
Author(s):  
Changben Yu ◽  
Jin Yang ◽  
Nan Song ◽  
Ci Sun ◽  
Mingjia Wang ◽  
...  
Keyword(s):  
Microlens Array ◽  
Biomedical Domain

scholarly journals Sentiment Analysis of Suicide Notes: A Shared Task

2012 ◽  
Vol 5s1 ◽  
pp. BII.S9042 ◽  
Author(s):  
John P. Pestian ◽  
Pawel Matykiewicz ◽  
Michelle Linn-Gust ◽  
Brett South ◽  
Ozlem Uzuner ◽  
...  
Keyword(s):  
Preliminary Analysis ◽  
The Other ◽  
Future Research ◽  
Large Set ◽  
Biomedical Domain ◽  
Shared Task ◽  
Clinical Text ◽  
Evaluation Measures ◽  
Suicide Notes ◽  
Data Production

This paper reports on a shared task involving the assignment of emotions to suicide notes. Two features distinguished this task from previous shared tasks in the biomedical domain. One is that it resulted in the corpus of fully anonymized clinical text and annotated suicide notes. This resource is permanently available and will (we hope) facilitate future research. The other key feature of the task is that it required categorization with respect to a large set of labels. The number of participants was larger than in any previous biomedical challenge task. We describe the data production process and the evaluation measures, and give a preliminary analysis of the results. Many systems performed at levels approaching the inter-coder agreement, suggesting that human-like performance on this task is within the reach of currently available technologies.

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