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General Architecture for Text Engineering (GATE) is a Java suite of natural language processing (NLP) tools for man tasks, including information extraction in many languages. [1] It is now used worldwide by a wide community of scientists, companies, teachers and students. It was originally developed at the University of Sheffield beginning in 1995.
It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, [6] plus a cookbook. [ 7 ] NLTK is intended to support research and teaching in NLP or closely related areas, including empirical linguistics , cognitive science , artificial intelligence , information retrieval , and ...
Its machine-learning ecosystem includes Nx for computing on CPUs and GPUs, Bumblebee and Axon for serving and training models, Broadway for distributed processing pipelines, Membrane for image and video processing, Livebook for prototyping and publishing notebooks, and Nerves for embedding on devices.
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.
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.
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.
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.
The design has its origins from pre-training contextual representations, including semi-supervised sequence learning, [24] generative pre-training, ELMo, [25] and ULMFit. [26] Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus .
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