  Recognizing and categorizing lexical collocations in context is useful for
language learning, dictionary compilation and downstream NLP. However, it is a
challenging task due to the varying degrees of frozenness lexical collocations
exhibit. In this paper, we put forward a sequence tagging BERT-based model
enhanced with a graph-aware transformer architecture, which we evaluate on the
task of collocation recognition in context. Our results suggest that explicitly
encoding syntactic dependencies in the model architecture is helpful, and
provide insights on differences in collocation typification in English, Spanish
and French.
