  Event Detection (ED) is an important task in natural language processing. In
the past few years, many datasets have been introduced for advancing ED machine
learning models. However, most of these datasets are under-explored because not
many tools are available for people to study events, trigger words, and event
mention instances systematically and efficiently. In this paper, we present an
interactive and easy-to-use tool, namely ED Explorer, for ED dataset and model
exploration. ED Explorer consists of an interactive web application, an API,
and an NLP toolkit, which can help both domain experts and non-experts to
better understand the ED task. We use ED Explorer to analyze a recent proposed
large-scale ED datasets (referred to as MAVEN), and discover several underlying
problems, including sparsity, label bias, label imbalance, and debatable
annotations, which provide us with directions to improve the MAVEN dataset. The
ED Explorer can be publicly accessed through http://edx.leafnlp.org/. The
demonstration video is available here
https://www.youtube.com/watch?v=6QPnxPwxg50.
