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The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.
Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020.. Like its predecessor, GPT-2, it is a decoder-only [2] transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". [3]
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.
Natural language processing—stemming is generally regarded as a form of NLP; NLTK—implements several stemming algorithms in Python; Root (linguistics)—linguistic definition of the term "root" Snowball (programming language)—designed for creating stemming algorithms; Stem (linguistics)—linguistic definition of the term "stem"
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]
Consequently, in NLP, the more commonly used measure is perplexity per token (word or, more frequently, sub-word), defined as: (= ()) / where ,..., are the documents in the corpus and is the number of tokens in the corpus. This normalizes the perplexity by the length of the text, allowing for more meaningful comparisons between different texts ...
A predecessor concept was used in creating some concordances.For example, the first Hebrew concordance, Isaac Nathan ben Kalonymus's Me’ir Nativ, contained a one-page list of unindexed words, with nonsubstantive prepositions and conjunctions which are similar to modern stop words.