Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics. It models language predominantly by way of theoretical syntactic/semantic theory (e.g. CCG, HPSG, LFG, TAG, the Prague School). Deep linguistic processing approaches differ from "shallower" methods in that they yield more expressive and structural representations which directly capture long-distance dependencies and underlying predicate-argument structures.[1]
The knowledge-intensive approach of deep linguistic processing requires considerable computational power, and has in the past sometimes been judged as being intractable. However, research in the early 2000s had made considerable advancement in efficiency of deep processing.[2][3] Today, efficiency is no longer a major problem for applications using deep linguistic processing.
^Timothy Baldwin, Mark Dras, Julia Hockenmaier, Tracy Holloway King, and Gertjan van Noord. 2007. The impact of deep linguistic processing on parsing technology. In Proc. of the 10th International Workshop on Parsing Technologies (IWPT-2007), pages 36–8, Prague, Czech Republic.
^Ulrich Callmeier. PET – A platform for experimentation with efficient HPSG processing techniques. Natural Language Engineering, 6(1):99 – 108, 2000.
^Hans Uszkoreit. New Chances for Deep Linguistic Processing Archived 2005-11-03 at the Wayback Machine. In Proceedings of COLING 2002, pages xiv–xxvii, Taipei, Taiwan, 2002.
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