Components and Architecture
Regardless of the approach used, some common components can be identified in most natural language understanding systems. The system needs a lexicon of the language and a parser and grammar rules to break sentences into an internal representation. The construction of a rich lexicon with a suitable ontology requires significant effort, e.g., the Wordnet lexicon required many person-years of effort.
The system also needs a semantic theory to guide the comprehension. The interpretation capabilities of a language understanding system depend on the semantic theory it uses. Competing semantic theories of language have specific trade offs in their suitability as the basis of computer automated semantic interpretation. These range from naive semantics or stochastic semantic analysis to the use of pragmatics to derive meaning from context.
Advanced applications of natural language understanding also attempt to incorporate logical inference within their framework. This is generally achieved by mapping the derived meaning into a set of assertions in predicate logic, then using logical deduction to arrive at conclusions. Systems based on functional languages such as Lisp hence need to include a subsystem for the representation of logical assertions, while logic oriented systems such as those using the language Prolog generally rely on an extension of the built in logical representation framework.
The management of context in natural language understanding can present special challenges. A large variety of examples and counter examples have resulted in multiple approaches to the formal modeling of context, each with specific strengths and weaknesses.
Read more about this topic: Natural Language Understanding
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