TY - GEN
T1 - Event Detection Explorer
T2 - 28th International Conference on Intelligent User Interfaces, IUI 2023
AU - Zhang, Wenlong
AU - Ingale, Bhagyashree
AU - Shabir, Hamza
AU - Li, Tianyi
AU - Shi, Tian
AU - Wang, Ping
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/3/27
Y1 - 2023/3/27
N2 - 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.
AB - 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.
KW - Event Detection
KW - Interactive Tool
KW - Natural Language Processing
UR - http://www.scopus.com/inward/record.url?scp=85151971116&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85151971116&partnerID=8YFLogxK
U2 - 10.1145/3581754.3584178
DO - 10.1145/3581754.3584178
M3 - Conference contribution
AN - SCOPUS:85151971116
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 171
EP - 174
BT - IUI 2023 - Companion Proceedings of the 28th International Conference on Intelligent User Interfaces
Y2 - 27 March 2023 through 31 March 2023
ER -