Enhancing automatic term recognition through recognition of variation

Terminological variation is an integral part of the linguistic ability to realise a concept in many ways, but it is typically considered an obstacle to automatic term recognition (ATR) and term management. We present a method that integrates term variation in a hybrid ATR approach, in which term candidates are recognised by a set of linguistic filters and termhood assignment is based on joint frequency of occurrence of all term variants. We evaluate the effectiveness of incorporating specific types of term variation by comparing it to the performance of a baseline method that treats term variants as separate terms. We show that ATR precision is enhanced by considering joint termhoods of all term variants, while recall benefits by the introduction of new candidates through consideration of different variation types. On a biomedical test corpus we show that precision can be increased by 20–70% for the top ranked terms, while recall improves generally by 2–25%.