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  2. Earley parser - Wikipedia

    en.wikipedia.org/wiki/Earley_parser

    DECLARE ARRAY S; function INIT (words) S ← CREATE_ARRAY (LENGTH (words) + 1) for k ← from 0 to LENGTH (words) do S [k] ← EMPTY_ORDERED_SET function EARLEY_PARSE (words, grammar) INIT (words) ADD_TO_SET ((γ → • S, 0), S [0]) for k ← from 0 to LENGTH (words) do for each state in S [k] do // S[k] can expand during this loop if not FINISHED (state) then if NEXT_ELEMENT_OF (state) is a ...

  3. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Natural Language Processing 2021 [114] Nguyen et al. PG-19: A set of books extracted from the Project Gutenberg books library Text Natural Language Processing 2019 Jack W et al. Deepmind Mathematics: Mathematical question and answer pairs. Text Natural Language Processing 2018 [115] D Saxton et al. Anna's Archive

  4. Natural-language programming - Wikipedia

    en.wikipedia.org/wiki/Natural-language_programming

    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.

  5. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    NLopt (C/C++ implementation, with numerous interfaces including Julia, Python, R, MATLAB/Octave), includes various nonlinear programming solvers SciPy (de facto standard for scientific Python) has scipy.optimize solver, which includes several nonlinear programming algorithms (zero-order, first order and second order ones).

  6. Natural Language Toolkit - Wikipedia

    en.wikipedia.org/wiki/Natural_Language_Toolkit

    The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. [4]

  7. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning. [24] In-context learning is an emergent ability [25] of large language models.

  8. For loop - Wikipedia

    en.wikipedia.org/wiki/For_loop

    In computer science, a for-loop or for loop is a control flow statement for specifying iteration. Specifically, a for-loop functions by running a section of code repeatedly until a certain condition has been satisfied. For-loops have two parts: a header and a body. The header defines the iteration and the body is the code executed once per ...

  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.