  Event Coreference Resolution (ECR) is the task of linking mentions of the
same event either within or across documents. Most mention pairs are not
coreferent, yet many that are coreferent can be identified through simple
techniques such as lemma matching of the event triggers or the sentences in
which they appear. Existing methods for training coreference systems sample
from a largely skewed distribution, making it difficult for the algorithm to
learn coreference beyond surface matching. Additionally, these methods are
intractable because of the quadratic operations needed. To address these
challenges, we break the problem of ECR into two parts: a) a heuristic to
efficiently filter out a large number of non-coreferent pairs, and b) a
training approach on a balanced set of coreferent and non-coreferent mention
pairs. By following this approach, we show that we get comparable results to
the state of the art on two popular ECR datasets while significantly reducing
compute requirements. We also analyze the mention pairs that are "hard" to
accurately classify as coreferent or non-coreferent. Code at
https://github.com/ahmeshaf/lemma_ce_coref
