Exploring terminological relations between multi-word terms in distributional semantic models

A term is a lexical unit with specialized meaning in a particular domain. Terms may be simple (STs) or multi-word (MWTs). The organization of terms gives a representation of the structure of domain knowledge, which is based on the relationships between the concepts of the domain. However, relations between MWTs are often underrepresented in terminology resources. This work aims to explore distributional semantic models for capturing terminological relations between multi-word terms through lexical substitution and analogy. The experiments show that the results of the analogy-based method are globally better than those of the one based on lexical substitution and that analogy is well suited to the acquisition of synonymy, antonymy, and hyponymy while lexical substitution performs best for hypernymy.