Recent years have seen a gradual shift of focus from entity-based tasks to event-based tasks
in information extraction research. Being a core event-based task, event coreference …

Event is a common but non-negligible knowledge type. How to identify events from texts,
extract their arguments, even analyze the relations between different events are important …

While joint models have been developed for many NLP tasks, the vast majority of event
coreference resolvers, including the top-performing resolvers competing in the recent TAC …

Event coreference resolution is an important research problem with many applications.
Despite the recent remarkable success of pretrained language models, we argue that it is …

Conventional event coreference systems commonly use a pipeline architecture and rely
heavily on handcrafted features, which often causes error propagation problems and leads …

The vast majority of existing work on learning-based event coreference resolution has
employed the so-called mentionpair model, which is a binary classifier that determines …

The SRCB team participated in Entity Discovery and Linking task and Event Nugget
Detection task in TAC Knowledge Base Population (KBP) 2017. The EDL system includes …

We present two extensions to a state-of-theart joint model for event coreference resolution,
which involve incorporating (1) a supervised topic model for improving trigger detection by …

This paper presents the CLUZH systems for the TAC KBP 2017 Event Nugget Detection
Task. We submit three runs in all three languages: English, Spanish, and Chinese. Each run …

Event coreference resolution is a challenging NLP task due to this task needs to understand
the semantics of events. Different with most previous studies used probability-based or …