Lin: Unsupervised Extraction of Tasks from Textual Communication
Parth Diwanji, Hui Guo, Munindar P. Singh, Anup K. Kalia · 2020
Commitments and requests are a hallmark of collaborative communication, especially in organizational settings.Identifying specific tasks being committed to or requests from emails and chat messages can enable important downstream tasks, such as producing to-do lists, reminders, and calendar entries.State-of-the-art approaches for task identification rely on large annotated datasets, which are not always available, especially for domain-specific tasks.Accordingly, we propose Liṅ, an unsupervised approach of identifying tasks that leverages dependency parsing and VerbNet.Our evaluations show that Liṅ yields comparable or more accurate results than supervised models on domains with large training sets, and maintains its excellent performance on unseen domains.