Results 51 to 60 of about 47,614 (155)
Using Neural Networks for Relation Extraction from Biomedical Literature
, 2020 Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems.
A Koike +34 more
core +1 more source
Cell line name recognition in support of the identification of synthetic lethality in cancer from text [PDF]
, 2015 Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature.
Ginter, Filip +5 more
core +2 more sources
Extending TextAE for annotation of non-contiguous entities [PDF]
Genomics & Informatics, 2020 Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems.
Jake Lever, Russ Altman, Jin-Dong Kim
doaj +1 more source
Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus [PDF]
, 2016 The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic ...
A Roberts +23 more
core +1 more source
Biomedical Named Entity Recognition: A Review
International Journal on Advanced Science, Engineering and Information Technology, 2016 Biomedical Named Entity Recognition (BNER) is the task of identifying biomedical instances such as chemical compounds, genes, proteins, viruses, disorders, DNAs and RNAs. The key challenge behind BNER lies on the methods that would be used for extracting such entities.
Basel Alshaikhdeeb, Kamsuriah Ahmad
openaire +2 more sources
Bootstrapping and evaluating named entity recognition in the biomedical domain [PDF]
Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology - LNLBioNLP '06, 2006 We demonstrate that bootstrapping a gene name recognizer for FlyBase curation from automatically annotated noisy text is more effective than fully supervised training of the recognizer on more general manually annotated biomedical text. We present a new test set for this task based on an annotation scheme which distinguishes gene names from gene ...
Andreas Vlachos 0001, Caroline Gasperin
openaire +3 more sources
BMC Bioinformatics, 2021 Background The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research.
Renzo M. Rivera-Zavala, Paloma Martínez
doaj +1 more source
UEM-UC3M: An Ontology-based named entity recognition system for biomedical texts [PDF]
, 2015 Proceedings of: International Workshop on Semantic Evaluation. SemEval-2013 : Semantic Evaluation Exercises. Took place in 2013 June, 14-15, in Atlanta, Georgia (USA).
Aparicio Gali, Fernando +1 more
core +2 more sources
ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition
Complexity, 2021 The web is being loaded daily with a huge volume of data, mainly unstructured textual data, which increases the need for information extraction and NLP systems significantly.
Nada Boudjellal +6 more
doaj +1 more source
Distilling Knowledge with a Teacher’s Multitask Model for Biomedical Named Entity Recognition
Information, 2023 Single-task models (STMs) struggle to learn sophisticated representations from a finite set of annotated data. Multitask learning approaches overcome these constraints by simultaneously training various associated tasks, thereby learning generic ...
Tahir Mehmood +4 more
doaj +1 more source