The explosive growth of biomedical literature has created a rich source of knowledge, such
as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in …

Traditional approaches to the task of ACE event extraction usually depend on manually
annotated data, which is often laborious to create and limited in size. Therefore, in addition …

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast
the task as a multi-turn question answering problem, ie, the extraction of entities and …

Biomedical relation extraction aims to uncover high‐quality relations from life science
literature with high accuracy and efficiency. Early biomedical relation extraction tasks …

Many joint entity relation extraction models setup two separated label spaces for the two sub-
tasks (ie, entity detection and relation classification). We argue that this setting may hinder …

This paper proposes a history-based structured learning approach that jointly extracts
entities and relations in a sentence. We introduce a novel simple and flexible table …

Most previous event extraction studies have relied heavily on features derived from
annotated event mentions, thus cannot be applied to new event types without annotation …

Neural networks have shown promising results for relation extraction. State-of-the-art
models cast the task as an end-to-end problem, solved incrementally using a local classifier …

We develop a new paradigm for the task of joint entity relation extraction. It first identifies
entity spans, then performs a joint inference on entity types and relation types. To tackle the …

The plethora of biomedical relations which are embedded in medical logs (records)
demands researchers' attention. Previous theoretical and practical focuses were restricted …