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Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
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]
Natural-language programming (NLP) is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English. [1] A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program .
Python is a high-level, general-purpose programming language that is popular in artificial intelligence. [1] It has a simple, flexible and easily readable syntax. [2] Its popularity results in a vast ecosystem of libraries, including for deep learning, such as PyTorch, TensorFlow, Keras, Google JAX.
AutoTutor is an intelligent tutoring system developed by researchers at the Institute for Intelligent Systems at the University of Memphis, including Arthur C. Graesser that helps students learn Newtonian physics, computer literacy, and critical thinking topics through tutorial dialogue in natural language.
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.
The transformer has had great success in natural language processing (NLP). Many large language models such as GPT-2, GPT-3, GPT-4, AlbertAGPT, Claude, BERT, XLNet, RoBERTa and ChatGPT demonstrate the ability of transformers to perform a wide variety of NLP-related subtasks and their related real-world applications, including: machine translation
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.