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In view of the complexity of language and the broad range of interest in studying it from different angles, it's clear that we have barely scratched the surface here.
Additionally, within NLP itself, there are many important methods and applications that we haven't mentioned.
In spite of all that has come before, language presents us with far more than a temporary challenge for computation.
Consider the following sentences which attest to the riches of language: Other evidence for the riches of language is the vast array of disciplines whose work centers on language.
Some obvious disciplines include translation, literary criticism, philosophy, anthropology and psychology.
Many less obvious disciplines investigate language use, including law, hermeneutics, forensics, telephony, pedagogy, archaeology, cryptanalysis and speech pathology.
All serve to deepen our understanding of language and of the intellect that is manifested in language.
By contrast, systems which involved learning patterns from large bodies of speech data were significantly more accurate, efficient and robust.
In addition, the speech community found that progress in building better systems was hugely assisted by the construction of shared resources for quantitatively measuring performance against common test data.
This principle provided a useful correspondence between syntax and semantics, namely that the meaning of a complex expression could be computed recursively. The approaches just outlined share the premise that computing with natural language crucially relies on rules for manipulating symbolic representations.
For a certain period in the development of NLP, particularly during the 1980s, this premise provided a common starting point for both linguists and practitioners of NLP, leading to a family of grammar formalisms known as unification-based (or feature-based) grammar (cf.
In our closing remarks we will take a broader view of NLP, including its foundations and the further directions you might want to explore.