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Conventional terminology resources reach their limits when it comes to automatic content classification of texts in the domain of expertlayperson communication. This can be attributed to the fact that (non-normalized) language usage does not necessarily reflect the terminological elements stored in such resources. We present several strategies to extend a terminological resource with term-related elements in order to optimize automatic content classification of expert-layperson texts.
In this paper, we present our approach to automatically extracting German terminology in the domain of grammar using texts from the online information system grammis as our corpus. We analyze existing repositories of German grammatical terminology and develop Part-of-speech patterns for our extraction thereby showing the importance of unigrams in this domain. We contrast the results of the automatic extraction with a manually extracted standard. By comparing the performance of well-known statistical measures, we show how measures based on corpus comparison outperform alternative methods.