Results 81 to 90 of about 331 (148)

GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition. [PDF]

Bioinformatics
Yazdani A, Stepanov I, Teodoro D.
europepmc   +1 more source

A study of deep active learning methods to reduce labelling efforts in biomedical relation extraction. [PDF]

PLoS One, 2023
Nachtegael C, De Stefani J, Lenaerts T.
europepmc   +1 more source

D2.5 public summary: Bio-based diols for BIORING

This is the public summary of deliverable 2.5, and presents the BIORING investigation on bio-based diols.
openaire   +1 more source

JTIS: enhancing biomedical document-level relation extraction through joint training with intermediate steps. [PDF]

Database (Oxford)
Li J, Pan D, Yang Z, Sun Y, Lin H, Wang J.   +5 more
europepmc   +1 more source

A pretrained biomedical large language model for Persian biomedical text mining. [PDF]

Sci Rep
Merzah BM, Taami T, Asoudeh S, Mirzaee S, Hossein Pour AR, Bengari AA, Shahmarvandi EK.   +6 more
europepmc   +1 more source

NER/RE strategies for biological graph completion [PDF]

Natural Language Processing (NLP), a subset of artificial intelligence, facili- tates computer understanding of human language, with techniques like Named Entity Recognition (NER) and Relation Extraction (RE) essential for trans- forming unstructured ...
Caballero Vergés, Biel
core  

Development of an Extended Biomedical Named Entity Recognition and Relation Extraction Model for Malaria using BioBERT [PDF]

This study aims to enhance the biomedical Named Entity Recognition and Relation Extraction model for use in the malaria subdomain by fine-tuning the existing BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining ...
AJAO, Ademolu Daniel, Covenant University, Theses Masters   +1 more
core  

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition. [PDF]

Int J Mol Sci, 2022
Ivanisenko TV, Demenkov PS, Kolchanov NA, Ivanisenko VA.   +3 more
europepmc   +1 more source

BioGSF: a graph-driven semantic feature integration framework for biomedical relation extraction. [PDF]

Brief Bioinform
Yang Y, Zheng Z, Xu Y, Wei H, Yan W.
europepmc   +1 more source

EMBRE: Entity-aware Masking for Biomedical Relation Extraction

Information extraction techniques, including named entity recognition (NER) and relation extraction (RE), are crucial in many domains to support making sense of vast amounts of unstructured text data by identifying and connecting relevant information ...
Li, Mingjie, Verspoor, Karin
core