Content deleted Content added
m simplify headings, cap |
→Contrast to "Shallow Linguistic Processing": Fixed the title Tags: Mobile edit Mobile web edit |
||
Line 2:
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.
==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.
| |||