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  2. Methods of neuro-linguistic programming - Wikipedia

    en.wikipedia.org/wiki/Methods_of_neuro...

    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.

  3. Spark NLP - Wikipedia

    en.wikipedia.org/wiki/Spark_NLP

    Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. [10] It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction.

  4. Outline of natural language processing - Wikipedia

    en.wikipedia.org/wiki/Outline_of_natural...

    AlchemyAPI – service provider of a natural-language processing API. Google, Inc. – the Google search engine is an example of automatic summarization, utilizing keyphrase extraction. Calais (Reuters product) – provider of a natural-language processing services.

  5. Bag-of-words model - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model

    It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity . The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a ...

  6. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    It is notable for its dramatic improvement over previous state-of-the-art models, and as an early example of a large language model. As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. [3] BERT is trained by masked token prediction and next sentence prediction.

  7. Semantic parsing - Wikipedia

    en.wikipedia.org/wiki/Semantic_parsing

    Semantic parsers play a crucial role in natural language understanding systems because they transform natural language utterances into machine-executable logical structures or programmes. A well-established field of study, semantic parsing finds use in voice assistants, question answering, instruction following, and code generation.

  8. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    In 2018, researchers first proposed that all previously separate tasks in natural language processing (NLP) could be cast as a question-answering problem over a context. In addition, they trained a first single, joint, multi-task model that would answer any task-related question like "What is the sentiment" or "Translate this sentence to German ...

  9. spaCy - Wikipedia

    en.wikipedia.org/wiki/SpaCy

    spaCy (/ s p eɪ ˈ s iː / spay-SEE) is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. [3] [4] The library is published under the MIT license and its main developers are Matthew Honnibal and Ines Montani, the founders of the software company Explosion.

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