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. [[Combinatory categorial grammar|CCG]], [[HPSG]], [[Lexical functional grammar|LFG]], [[Tree-adjoining grammar|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 (grammar)|predicate]]-[[argument]] structures.<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> <br/>

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 efficiencyefficiency of deep processing.<ref>Ulrich Callmeier. [http://dl.acm.org/citation.cfm?id=973952.973959 PET – A platform for experimentation with efficientefficient HPSG processing techniques]. Natural Language Engineering, 6(1):99 – 108, 2000.</ref><ref>Hans Uszkoreit. [http://acl.ldc.upenn.edu/coling2002/proceedings/data/area-01/uszkoreit.pdf New Chances for Deep Linguistic Processing]. In Proceedings of COLING 2002, pages xiv–xxvii, Taipei, Taiwan, 2002.</ref> Today, efficiencyefficiency is no longer a major problem for applications using deep linguistic processing.

==Contrast to "shallow linguistic processing"==

Traditionally, deep linguistic processing has been concerned with computational grammar development (for use in both [[parsing]] and generation). These grammars 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. Thus deep linguistic processing methods have received less attention.

However, it is the belief of some computational linguists{{Who|date=August 2012}} that in order for computers to understand natural language or [[inference]], detailed syntactic and [[Semantic analysis (knowledge representation)|semantic representation]] is necessary. Moreover, shallow methods may lack human language 'understanding'. While humans can easily understand a sentence and its meaning, shallow linguistic processing might lack human language 'understanding'. For example:<ref>U. Schafer. 2007. ¨ [http://scidok.sulb.uni-saarland.de/volltexte/2007/1326/pdf/Dissertation_1383_Schae_Ulri_2007.pdf Integrating Deep and Shallow Natural Language Processing Components – Representations and Hybrid Architectures]. Ph.D. thesis, Faculty of Mathematics and Computer Science, Saarland University, Saarbrucken, Germany.</ref> <br/>