Deep linguistic processing

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. 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.

Deep vs Shallow Linguistic Processing

Traditionally, deep linguistic processing has been concerned with computational grammar development (for use in both parsing and generation). These grammar 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 that in order for computers to understand natural language or inference, detailed syntactic and semantic representation is necessary.