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More commonly, question-answering systems can pull answers from an unstructured collection of natural language documents. Some examples of natural language document collections used for question answering systems include: a local [clarification needed] collection of reference texts; internal organization [ambiguous] documents and web pages
Concepts in an NLP are examples (samples) of generic human concepts. Each sentence in a natural-language program is either (1) stating a relationship in a world model or (2) carries out an action in the environment or (3) carries out a computational procedure or (4) invokes an answering mechanism in response to a question.
NLP makes use of computers, image scanners, microphones, and many types of software programs. Language technology – consists of natural-language processing (NLP) and computational linguistics (CL) on the one hand, and speech technology on the other. It also includes many application oriented aspects of these.
We are now reaching a sort of tipping point where we will see many more commercial applications of NLP — some using some of these open source, publicly available platforms — hit the market.
The methods of neuro-linguistic programming are the specific techniques used to perform and teach neuro-linguistic programming, [1] [2] which teaches that people are only able to directly perceive a small part of the world using their conscious awareness, and that this view of the world is filtered by experience, beliefs, values, assumptions, and biological sensory systems.
Annotated information flow diagram. An information flow diagram (IFD) is a diagram that shows how information is communicated (or "flows") from a source to a receiver or target (e.g. A→C), through some medium. [1]: 36–39 The medium acts as a bridge, a means of transmitting the information. Examples of media include word of mouth, radio ...
A natural-language search engine would in theory find targeted answers to user questions (as opposed to keyword search). For example, when confronted with a question of the form 'which U.S. state has the highest income tax?', conventional search engines ignore the question and instead search on the keywords 'state', 'income' and 'tax'.
Shallow semantic parsing is concerned with identifying entities in an utterance and labelling them with the roles they play. Shallow semantic parsing is sometimes known as slot-filling or frame semantic parsing, since its theoretical basis comes from frame semantics, wherein a word evokes a frame of related concepts and roles.