  We present an approach to event coreference resolution by developing a
general framework for clustering that uses supervised representation learning.
We propose a neural network architecture with novel Clustering-Oriented
Regularization (CORE) terms in the objective function. These terms encourage
the model to create embeddings of event mentions that are amenable to
clustering. We then use agglomerative clustering on these embeddings to build
event coreference chains. For both within- and cross-document coreference on
the ECB+ corpus, our model obtains better results than models that require
significantly more pre-annotated information. This work provides insight and
motivating results for a new general approach to solving coreference and
clustering problems with representation learning.
