We consider event mentions as the data points and encode the similarity (or compatibility) between event mentions as priors for event clustering.
we use a log-linear model to estimate the similarity between a pair of event mentions ( where ψ is a feature vector, containing a rich set of features based on event mentions i and j: (1) head word string match, (2) head POS pair, (3) cosine similarity between the head word embeddings (we use the pre-trained 300-dimensional word embeddings from word2vec 1 ), (4) similarity between the words in the event mentions (based on term frequency (TF) vectors), (5) the Jaccard coefficient between the WordNet synonyms of the head words, and (6) similarity between the context words (a window of three words before and after each event mention).