It models language predominantly by way of theoretical syntactic/semantic theory (e.g. CCG, HPSG, LFG, TAG, the Prague School).
[1] The knowledge-intensive approach of deep linguistic processing requires considerable computational power, and has in the past sometimes been judged as being intractable.
The rapid creation of robust and wide-coverage machine learning NLP tools requires substantially lesser amount of manual labor.
For example:[4] In sentence (a), a shallow information extraction system might infer wrongly that Microsoft's headquarters was located in Georgia.
The major sub-communities includes the: The shortlist above is not exhaustively representative of all the communities working on deep linguistic processing.