Event extraction requires high-quality expert human annotations, which are usually
expensive. Therefore, learning a data-efficient event extraction model that can be trained …

The past decade has witnessed an explosion of textual information in the biomedical field.
Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision …

This book provides an overview of the recent advances in representation learning theory,
algorithms, and applications for natural language processing (NLP), ranging from word …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Abstract The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation
(AMR), share similar goals with Information Extraction (IE) to convert natural language texts …

Compared to the general news domain, information extraction (IE) from biomedical text
requires much broader domain knowledge. However, many previous IE methods do not …

Recent progress in cross-lingual relation and event extraction use graph convolutional
networks (GCNs) with universal dependency parses to learn language-agnostic sentence …

Motivation: The scientific literature embeds an enormous amount of relational knowledge,
encompassing interactions between biomedical entities, like proteins, drugs, and symptoms …

Automatically summarizing patients' main problems from daily progress notes using natural
language processing methods helps to battle against information and cognitive overload in …

Event extraction has gained considerable interest due to its wide-ranging applications.
However, recent studies draw attention to evaluation issues, suggesting that reported scores …