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6. SUMMARY AND CONCLUSIONIn this chapter, we have examined the Natural Language Processing (NLP) techniques that can be of some utility in an expert system (and, more generally, knowledge-based system) context. After a short historical introduction, we recalled the traditional differentiation of the NLP domain into morphological analysis, syntactic analysis, semantic analysis, discourse, and pragmatic analysis -- even if this sort of distinction becomes sometimes blurred in modern systems (see, e.g., the dialog, and the Spoken Language Understanding systems), it is still particularly effective from a didactic point of view. For each of these subfields, we have analyzed quickly the most important techniques used, devoting some additional space to particularly "hot" topics like unification grammars in syntax and conceptual representation in semantics. A section mentioning some of the most promising trends in the NLP domain concludes the chapter. The NLP domain has changed dramatically over recent years. Some, not necessarily trivial, commercial NLP products are now on the market, and it is easy to anticipate the fact that future ESs and KBSs will increasingly integrate components produced by the NLP technology. The success of pragmatically based applications such as extraction of information from texts or speech recognition systems has moved the focus from pure theoretical research, oriented toward the production of toy systems in very limited domains, to applied research largely motivated by industrial considerations, and oriented toward the construction of larger-scale NLP systems. Even if NLP still constitutes one of the more complex and difficult endeavors in computer science, the domain seems now sufficiently ripe to contribute to the establishment and to the consolidation of a concrete, sound, and human-friendly computer technology for the next millennium. REFERENCES
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