The　identification　of　emerging　technologies　can　bring　valuable　intelligence　to　enterprises　and　countries　determining　research　and　development　(R&D)　priorities.　Emerging　technologies　are　closely　related　to　emerging　topics　in　terms　of　several　well-documented　attributes:　relatively　fast　growth,　radical　novelty　and　prominent　impact.　Our　previous　work　on　detecting　and　forecasting　emerging　topics　is　adapted　to　measure　technology　emergence,　but　the　dynamic　influence　model　(DIM)　is　replaced　by　the　topical　n-grams　(TNG)　model　in　this　framework　to　nominate　several　emerging　technologies　in　technical　terms　and　to　exploit　the　potential　of　topic　models.　Hence,　technologies　are　viewed　as　term-based　themes　in　this　study.　Three　indicators　are　designed　to　reflect　the　above　attributes:　the　fast　growth　indicator,　the　radical　novelty　indicator　and　the　prominent　impact　indicator.　The　relatively　fast　growth　indicator　is　calculated　from　the　results　of　the　TNG　model　and　the　radical　novelty　indicator　comes　from　the　citation　influence　model　(CIM).　As　for　the　prominent　impact　indicator,　the　involving　authors　are　used　after　name　disambiguation　and　credit　allocation.　The　following　fields　are　utilized　to　develop　the　models:　title,　abstract,　keywords-author,　publication　year,　byline　information,　and　cited　references.　We　participated　in　the　20182019　Measuring　Tech　Emergence　Contest　with　the　proposed　method,　and　8　out　of　10　submitted　ones　met　the　contest　organizer＇s　criteria　of　technology　emergence.　Criteria　included　the　percentage　of　high　growth　terms　out　of　total　terms　provided,　the　degree　of　growth　of　the　terms,　and　the　frequency　of　those　high　growth　terms　across　the　dataset.　Then,　a　qualitative　assessment　of　overall　methodology　was　conducted　by　three　judges.　In　the　end,　we　won　Second　Prize　in　the　contest.　©　2020　Elsevier　Inc.