Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

Named Entity Recognition (NER) is a key component in NLP systems for question
answering, information retrieval, relation extraction, etc. NER systems have been studied …

Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in
learning distributed representations from graph-structured data. However, centralizing a …

Natural language processing (NLP) is crucial in the current processing of data because it
takes into account many sources, formats, and purposes of data as well as information from …

Background With the rapid adoption of electronic health records (EHRs), it is desirable to
harvest information and knowledge from EHRs to support automated systems at the point of …

Despite the fact that deep learning has achieved remarkable success in various domains
over the past decade, its application in molecular informatics and drug discovery is still …

Abstract Most Reading Comprehension methods limit themselves to queries which can be
answered using a single sentence, paragraph, or document. Enabling models to combine …

The number of published materials science articles has increased manyfold over the past
few decades. Now, a major bottleneck in the materials discovery pipeline arises in …

Motivation In biomedical research, chemical is an important class of entities, and chemical
named entity recognition (NER) is an important task in the field of biomedical information …

Background Word embeddings have been prevalently used in biomedical Natural
Language Processing (NLP) applications due to the ability of the vector representations …