Example-based acquisition of fine-grained collocational resources

Abstract

Collocations such as heavy rain or make [a] decision, are combinations of two elements where one (the base) is freely chosen, while the/nchoice of the other (collocate) is restricted, depending on the base. Collocations present difficulties even to advanced language learners,/nwho usually struggle to find the right collocate to express a particular meaning, e.g., both heavy and strong express the meaning ‘intense’,/nbut while rain selects heavy, wind selects strong. Lexical Functions (LFs) describe the meanings that hold between the elements of/ncollocations, such as ‘intense’, ‘perform’, ‘create’, ‘increase’, etc. Language resources with semantically classified collocations would/nbe of great help for students, however they are expensive to build, since they are manually constructed, and scarce. We present an unsupervised/napproach to the acquisition and semantic classification of collocations according to LFs, based on word embeddings in which,/ngiven 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.