Building a PubMed knowledge graph
PubMed® is an essential resource for the medical domain, but useful concepts are either
difficult to extract or are ambiguous, which has significantly hindered knowledge discovery …
difficult to extract or are ambiguous, which has significantly hindered knowledge discovery …
A neural network multi-task learning approach to biomedical named entity recognition
Abstract Background Named Entity Recognition (NER) is a key task in biomedical text
mining. Accurate NER systems require task-specific, manually-annotated datasets, which …
mining. Accurate NER systems require task-specific, manually-annotated datasets, which …
CliCR: a dataset of clinical case reports for machine reading comprehension
We present a new dataset for machine comprehension in the medical domain. Our dataset
uses clinical case reports with around 100,000 gap-filling queries about these cases. We …
uses clinical case reports with around 100,000 gap-filling queries about these cases. We …
Comparison of biomedical relationship extraction methods and models for knowledge graph creation
N Milošević, W Thielemann - Journal of Web Semantics, 2023 - Elsevier
Biomedical research is growing at such an exponential pace that scientists, researchers,
and practitioners are no more able to cope with the amount of published literature in the …
and practitioners are no more able to cope with the amount of published literature in the …
HUNER: improving biomedical NER with pretraining
Motivation Several recent studies showed that the application of deep neural networks
advanced the state-of-the-art in named entity recognition (NER), including biomedical NER …
advanced the state-of-the-art in named entity recognition (NER), including biomedical NER …
The COVID-19 pandemic and changes in the level of contact between older parents and their non-coresident children: A European study
Objective: The present study aims to investigate changes in the frequency of parent-child
contact among Europeans aged 65 years and over within the context of the COVID-19 …
contact among Europeans aged 65 years and over within the context of the COVID-19 …
S1000: a better taxonomic name corpus for biomedical information extraction
Motivation The recognition of mentions of species names in text is a critically important task
for biomedical text mining. While deep learning-based methods have made great advances …
for biomedical text mining. While deep learning-based methods have made great advances …
Brief description of covid-see: The scientific evidence explorer for covid-19 related research
We present COVID-SEE, a system for medical literature discovery based on the concept of
information exploration, which builds on several distinct text analysis and natural language …
information exploration, which builds on several distinct text analysis and natural language …
[PDF][PDF] Relationship extraction for knowledge graph creation from biomedical literature
N Milosevic, W Thielemann - arxiv preprint arxiv:2201.01647, 2022 - academia.edu
Biomedical research is growing in such an exponential pace that scientists, researchers and
practitioners are no more able to cope with the amount of published literature in the domain …
practitioners are no more able to cope with the amount of published literature in the domain …
Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine
Background Word representations support a variety of Natural Language Processing (NLP)
tasks. The quality of these representations is typically assessed by comparing the distances …
tasks. The quality of these representations is typically assessed by comparing the distances …