Jiarui Yao, Haoling Qiu, Jin Zhao, Bonan Min, Nianwen Xue. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

We present a system which allows a user to create event-event relation extractors on-demand with a small amount of effort. The system provides a suite of algorithms, flexible workflows, and a user interface (UI), to allow rapid customization of event-event relation extractors for new types and domains of interest. Experiments show that it enables users to create extractors for 6 types of causal and temporal relations, with less than 20 minutes of effort per type. Our system (source code, UI) is available at https://github.com/BBN-E/LearnIt. A demonstration video is available at https://vimeo.com/329950144.

We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text.We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good balance between completeness and practicality in temporal annotation.We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set.This data set is publicly available 1 .

Bonan Min, Yee Seng Chan, Haoling Qiu, Joshua Fasching. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.