Event Detection Explorer: An Interactive Tool for Event Detection Exploration
Wenlong Zhang, Bhagyashree Ingale, Hamza Shabir, Tianyi Li, Tian Shi, Ping Wang · 2023
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, 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 ED tasks. We use ED Explorer to analyze a recently proposed large-scale ED dataset (referred to as MAVEN). With ED Explorer, we discovered several underlying issues of the dataset, including data sparsity, label bias, label imbalance, and debatable annotations. Such insights are essential for guiding the continuous improvement of existing ED datasets and the advances of ED models. The ED Explorer system1 and the demonstration video2 have both been made publicly available.