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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.
[k] While some NLP practitioners have argued that the lack of empirical support is due to insufficient research which tests NLP, [l] the consensus scientific opinion is that NLP is pseudoscience [m] [n] and that attempts to dismiss the research findings based on these arguments "[constitute]s an admission that NLP does not have an evidence base ...
Definition of an ontology – taxonomy – of concepts needed to describe tasks in the topic addressed. Each concept and all their attributes are defined in natural-language words. This ontology will define the data structures the NLP can use in sentences. Definition of one or more top-level sentences in terms of concepts from the ontology.
Working concepts and methods within NLP. Note: This category does not contain overviews of NLP, or descriptions of individual systems based upon NLP, but purely concepts, methods, techniques and "building blocks" within NLP.
So for example a person that most highly values their visual representation system is able to easily and vividly visualise things, and has a tendency to do this more often than recreating sounds, feelings, etc. Representational systems are one of the foundational ideas of NLP and form the basis of many NLP techniques and methods. [7]
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.
Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively.
An example of a word-based translation system is the freely available GIZA++ package , which includes the training program for IBM models and HMM model and Model 6. [7] The word-based translation is not widely used today; phrase-based systems are more common. Most phrase-based systems are still using GIZA++ to align the corpus [citation needed].