As more and more Arabic texts emerged on the Internet, extracting important information
from these Arabic texts is especially useful. As a fundamental technology, Named entity …

To enhance phenotype recognition in clinical notes of genetic diseases, we developed two
models—PhenoBCBERT and PhenoGPT—for expanding the vocabularies of Human …

Few-shot NER needs to effectively capture information from limited instances and transfer
useful knowledge from external resources. In this paper, we propose a self-describing …

Abstract Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained
models with relation triples injecting from knowledge graphs to improve language …

Systematic literature review (SLR) is a crucial method for clinicians and policymakers to
make their decisions in a flood of new clinical studies. Because manual literature screening …

Weakly supervised learning is a popular approach for training machine learning models in
low-resource settings. Instead of requesting high-quality yet costly human annotations, it …

Biomedical relation extraction (BioRE) is the task of automatically extracting and classifying
relations between two biomedical entities in biomedical literature. Recent advances in …

We study the problem of query attribute value extraction, which aims to identify named
entities from user queries as diverse surface form attribute values and afterward transform …

While distant supervision has been extensively explored and exploited in NLP tasks like
named entity recognition, a major obstacle stems from the inevitable noisy distant labels …

Background Computational text phenotyping is the practice of identifying patients with
certain disorders and traits from clinical notes. Rare diseases are challenging to be …