Deep linguistic processing: Difference between revisions

Revision as of 05:55, 28 August 2012

Revision as of 06:21, 28 August 2012

Line 5:

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>

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