Deep Active Learning for Data Mining from Conflict Text Corpora
Mihai Croicu
High-resolution event data on armed conflict and related processes have
revolutionized the study of political contention with datasets like UCDP GED,
ACLED etc. However, most of these datasets limit themselves to collecting
spatio-temporal (high-resolution) and intensity data. Information on dynamics,
such as targets, tactics, purposes etc. are rarely collected owing to the
extreme workload of collecting data. However, most datasets rely on a rich
corpus of textual data allowing further mining of further information connected
to each event. This paper proposes one such approach that is inexpensive and
high performance, leveraging active learning - an iterative process of
improving a machine learning model based on sequential (guided) human input.
Active learning is employed to then step-wise train (fine-tuning) of a large,
encoder-only language model adapted for extracting sub-classes of events
relating to conflict dynamics. The approach shows performance similar to human
(gold-standard) coding while reducing the amount of required human annotation
by as much as 99%.