Example-based Acquisition of Fine-grained Collocation Resources

Collocations such as {``}heavy rain{''} or {``}make [a] decision{''}, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, depending on the base. Collocations present difficulties even to advanced language learners, who usually struggle to find the right collocate to express a particular meaning, e.g., both {``}heavy{''} and {``}strong{''} express the meaning {`}intense{'}, but while {``}rain{''} selects {``}heavy{''}, {``}wind{''} selects {``}strong{''}. Lexical Functions (LFs) describe the meanings that hold between the elements of collocations, such as {`}intense{'}, {`}perform{'}, {`}create{'}, {`}increase{'}, etc. Language resources with semantically classified collocations would be of great help for students, however they are expensive to build, since they are manually constructed, and scarce. We present an unsupervised approach to the acquisition and semantic classification of collocations according to LFs, based on word embeddings in which, given an example of a collocation for each of the target LFs and a set of bases, the system retrieves a list of collocates for each base and LF.