The future of natural-language processing
Ontology: A formal, explicit specification of how to represent the objects, concepts, and other entities in a particular system, as well as the relationships between them
Natural-language processing (NLP) is an area of artificial intelligence research that attempts to reproduce the human interpretation of language. NLP methodologies and techniques assume that the patterns in grammar and the conceptual relationships between words in language can be articulated scientifically. The ultimate goal of NLP is to determine a system of symbols, relations, and conceptual information that can be used by computer logic to implement artificial language interpretation.
Natural-language processing has its roots in semiotics, the study of signs. Semiotics was developed by Charles Sanders Peirce (a logician and philosopher) and Ferdinand de Saussure (a linguist). Semiotics is broken up into three branches: syntax, semantics, and pragmatics.
A complete natural-language processor extracts meaning from language on at least seven levels. However, we'll focus on the four main levels.
Morphological: A morpheme is the smallest part of a word that can carry a discrete meaning. Morphological analysis works with words at this level. Typically, a natural-language processor knows how to understand multiple forms of a word: its plural and singular, for example.
Syntactic: At this level, natural-language processors focus on structural information and relationships.
Semantic: Natural-language processors derive an absolute (dictionary definition) meaning from context.
Pragmatic: Natural-language processors derive knowledge from external commonsense information.
A practical reality?
The realization of a fully communicating artificial intelligence was long considered a science fiction fantasy. However, with the advent of the World Wide Web, XML, and the World Wide Web Consortium's (W3C) RDF, NLP could become a pervasive reality. With powerful Web crawlers needing to index an exponentially growing collection of resources, it's no surprise that information management and data querying is an area that might benefit immensely from NLP.
So, why hasn't NLP escaped a backdrop of impractical artificial intelligence software implementations? How does XML technology fit into all this?
Natural-language limitations
One of the major limitations of modern NLP is that most linguists approach NLP at the pragmatic level by gathering huge amounts of information into large knowledge bases that describe the world in its entirety. These academic knowledge repositories are defined in ontologies that take on a life of their own and never end up in practical, widespread use. There are various knowledge bases, some commercial and some academic. The largest and most ambitious is the Cyc Project. The Cyc Knowledge Server is a monstrous inference engine and knowledge base. Even natural-language modules that perform specific, limited, linguistic services aren't financially feasible for use by the average developer.
In general, NLP faces the following challenges:
- Physical limitations: The greatest challenge to NLP is representing a sentence or group of concepts with absolute precision. The realities of computer software and hardware limitation make this challenge nearly insurmountable. The realistic amount of data necessary to perform NLP at the human level requires a memory space and processing
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