Cluster algorithm to group events in more general domains
Some creative post-processing can be done. For instance applying Named Entity Recognition and simplify some parts (Manchester is a City). Using Knowledge Graph Analysis also gives some meta-info e.g. mapping your word to Wikipedia graph or using DBpedia may help you to recognize Named Entitiy categories (Obama and Merkel are politicians and NER does not necessarily capture their profession).

Note that the combination of named entity recognition and knowledge-base (Wikipedia, DBpedia, etc.) mapping is called Entity Linking.

Regardless of statistical learning for NLP techniques, all structures above are actually graphs so they can give you also the semantic similarity measure based on which you can use other clustering algorithms like Spectral Clustering and go to a semantically higher level of clustering.

Hope it helps :)