Team: Wenlong Zhang, Bhagyashree Ingale, Hamza Shabir, Tianyi Li, Tian Shi, Ping Wang
About: Event Detection (ED) is an important task in natural language processing.
In the past few years, many datasets have been introduced for advancing machine learning models for ED task.
However, most of these datasets are under-explored because few tools are available for people to study events, trigger words, and event mention instances systematically and efficiently.
In this work, 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 a NLP toolkit, which can help both domain experts and non-experts to better understand the ED task.
We use ED Explorer to manually analyze a recent proposed large scale ED datasets (namely, MAVEN), and discover several underlying problems, including sparsity, label bias, label imbalance, and annotation errors.