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_grammarCombinatory |categorial grammar|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> <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 efficiency of deep processing.<ref>Ulrich Callmeier. [http://dl.acm.org/citation.cfm?id=973952.973959 PET – A platform for experimentation with efficient 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, efficiency is no longer a major problem for applications using deep linguistic processing.

==Deep vs 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.

<br>However it is the belief of some computational linguists that in order for computers to understand natural language or [[inference]], detailed syntactic and [[Semantic_analysis_Semantic analysis (knowledge_representationknowledge representation)|semantic representation]] is necessary. Moreover, shallow methods may lack human language 'understanding'. While humans can easily understand a sentence and it's 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>

:a) ''Things would be different if Microsoft was located in Georgia.'' <br>

In sentence A, a shallow [[information extraction]] system might infer wronlgy that Microsoft's headquarters was located in Georgia. While as humans, we understand from the sentence that Mircosoft office was never in Georgia.<br>

:b) ''The National Institue for Psychology in Israel was established in May 1971 as the Israel Center for Psychobiology by Prof. Joel.''

In sentence B, a shallow system could wrongly infer that Israel was establish in May 1971. Instead, humanly we know that it is the National Institute for Psychobiology that was establish in 1971.<br>

In summary of the comparison between deep and shallow language processing, deep linguistic processing provides a knowledge-rich analysis of language through manually developed grammars and language resources. Whereas, shallow linguistic processing provides a knowledge-lean analysis of language through statistical/machine learning manipulation of texts and/or [[Annotation |annotated lingusitic]] resource.

==Deep Linguistic Processing Sub-communities==

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*[[Natural language processing]]

*[[Head-driven Phrase Structure Grammar]]

*[[Combinatory_categorial_grammarCombinatory |categorial grammar|Combinatory Categorial Grammar]]

*[[Lexical_functional_grammarLexical |functional grammar|Lexical Functional Grammar]]

*[[Tree-adjoining_grammaradjoining grammar| Tree Adjoining Grammar]]

==References==

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[[Category:Natural language processing]]