Deep linguistic processing: Difference between revisions

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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]]). The Deep Linguistic Processing approaches differ from shallower methods in that they yield richer, more expressive, structural representation which capture [[long-distance dependencies]] or the underlying [[predicate]]-[[arguement]] structure directly.<ref>Timothy Baldwin, Mark Dras, Julia Hockenmaier, Tracy Holloway King, and Gertjan van Noord. 2007. [http://dl.acm.org/citation.cfm?id=1621410.1621415 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.</ref>

==Deep vs Shallow Linguistic Processing==

Traditionally, deep linguistic processing has been concerned with computational grammar development (for use in both [[parsing]] and generation). These grammar were manually developed, maintained and were computationally expensive to run. In recent years, machine learning approaches (also known as [[shallow linguistic processing]]) have fundamentally altered the field of [[natural language processing]]. The rapid creation of robust and wide-coverage machine learning NLP tools requires substantially lesser amount of manual labor. Also