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MALLET is an integrated collection of Java code useful for statistical natural language processing, document classification, cluster analysis, information extraction, topic modeling and other machine learning applications to text.
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.
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 .
NMT models are usually trained to maximize the likelihood of observing the training data. I.e., for a dataset of source sentences = (),..., and corresponding target sentences = (),..., (), the goal is finding the model parameters that maximize the sum of the likelihood of each target sentence in the training data given the corresponding source ...
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.
ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform. Smalltalk has been used extensively for simulations, neural networks, machine learning, and genetic algorithms. It implements a pure and elegant form of object-oriented programming using message passing.
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.
Note: This category does not contain overviews of NLP, or descriptions of individual systems based upon NLP, but purely concepts, methods, techniques and "building blocks" within NLP. Pages in category "Neuro-linguistic programming concepts and methods"