Adapting Term Recognition to an Under-Resourced Language: the Case of Irish

Automatic Term Recognition (ATR) is an important method for the summarization and analysis of large corpora, and normally requires a significant amount of linguistic input, in particular the use of part-of-speech taggers. For an under-resourced language such as Irish, the resources necessary for this may be scarce or entirely absent. We evaluate two methods for the automatic extraction of terms, based on the small part-of-speech-tagged corpora that are available for Irish and on a large terminology list, and show that both methods can produce viable term extractors. We evaluate this with a newly constructed corpus that is the first available corpus for term extraction in Irish. Our results shine some light on the challenge of adapting natural language processing systems to under-resourced scenarios.