  The problem of event extraction is a relatively difficult task for low
resource languages due to the non-availability of sufficient annotated data.
Moreover, the task becomes complex for tail (rarely occurring) labels wherein
extremely less data is available. In this paper, we present a new dataset
(InDEE-2019) in the disaster domain for multiple Indic languages, collected
from news websites. Using this dataset, we evaluate several rule-based
mechanisms to augment deep learning based models. We formulate our problem of
event extraction as a sequence labeling task and perform extensive experiments
to study and understand the effectiveness of different approaches. We further
show that tail labels can be easily incorporated by creating new rules without
the requirement of large annotated data.
