Deep linguistic processing

<p>Deep linguistic processing is a <a href="/facts/Natural_language_processing/1hjMKsSN">natural language processing</a> framework which draws on theoretical and <a href="/facts/Descriptive_linguistics/Hs0halB8">descriptive linguistics</a>. It models language predominantly by way of theoretical syntactic/semantic theory (e.g. <a href="/facts/Combinatory_categorial_grammar/UgtLoTG5">CCG</a>, <a href="/facts/HPSG/MW1TXsca">HPSG</a>, <a href="/facts/Lexical_functional_grammar/UqAfrL1G">LFG</a>, <a href="/facts/Tree-adjoining_grammar/gfr3weUw">TAG</a>, the <a href="/facts/Prague_School/sIbh1813">Prague School</a>). Deep linguistic processing approaches differ from "shallower" methods in that they yield more expressive and structural representations which directly capture <a href="/facts/Long-distance_dependencies/CXYguDTp">long-distance dependencies</a> and underlying <a href="/facts/Predicate_(grammar)/v1hK1EL2">predicate</a>-<a href="/facts/Argument/1K1e3Xg4">argument</a> structures. 
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. Today, efficiency is no longer a major problem for applications using deep linguistic processing.
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Deep linguistic processing open-in-new

Deep linguistic processing