Automated relation extraction (RE) from biomedical literature is critical for many downstream
text mining applications in both research and real-world settings. However, most existing …

As an essential component of human cognition, cause–effect relations appear frequently in
text, and curating cause–effect relations from text helps in building causal networks for …

Background Extracting biomedical entities and their relations from text has important
applications on biomedical research. Previous work primarily utilized feature-based pipeline …

Many shallow natural language understanding tasks use dependency trees to extract
relations between content words. However, strict surface-structure dependency trees tend to …

The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drugdrug
interactions that appear in biomedical literature. We propose two subtasks for the …

Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly developing branch of learning on structured data. During the past 20 years, the …

Irregular workloads are typically bottlenecked by the memory system. These workloads often
use sparse data representations, eg, compressed sparse row/column (CSR/CSC), to …

Identifying unknown drug interactions is of great benefit in the early detection of adverse
drug reactions. Despite existence of several resources for drug–drug interaction (DDI) …

Biomedical relation extraction can automatically extract high-quality biomedical relations
from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in …

Detecting protein-protein interactions (PPIs) is crucial for understanding genetic
mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth …