This study aims to show that frequency of occurrence over time for technical terms and keyphrases differs from general language terms in the sense that technical terms and keyphrases show a strong tendency to be recent coinage, and that this difference can be exploited for the automatic identification and extraction of technical terms and keyphrases. To this end, we propose two features extracted from temporally labelled datasets designed to capture surface level n-gram neology. Our analysis

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... ws that these features, calculated over consecutive bigrams, are highly indicative of technical terms and keyphrases, which suggests that both technical terms and keyphrases are strongly biased to be surface level neologisms. Finally, we evaluate the proposed features on a gold-standard dataset for technical term extraction and show that the proposed features are comparable or superior to a number of features commonly used for technical term extraction.