Outline of natural language processing
Updated: 5/24/2026, 6:50:40 PM Wikipedia source
Natural language processing is computer activity in which computers are entailed to analyze, understand, alter, or generate natural language. This includes the automation of any or all linguistic forms, activities, or methods of communication, such as conversation, correspondence, reading, written composition, dictation, publishing, translation, lip reading, and so on. Natural-language processing is also the name of the branch of computer science, artificial intelligence, and linguistics concerned with enabling computers to engage in communication using natural language(s) in all forms, including but not limited to speech, print, writing, and signing. The following outline is provided as an overview of and topical guide to natural-language processing:
Tables
| Name | Language | License | Creators |
| Apertium | C++, Java | GPL | (various) |
| ChatScript | C++ | GPL | Bruce Wilcox |
| Deeplearning4j | Java, Scala | Apache 2 | Adam Gibson, Skymind |
| DELPH-IN | LISP, C++ | LGPL, MIT, ... | Deep Linguistic Processing with HPSG Initiative |
| Distinguo | C++ | Commercial | Ultralingua Inc. |
| DKPro Core | Java | Apache 2 / Varying for individual modules | Technische Universität Darmstadt / Online community |
| General Architecture for Text Engineering (GATE) | Java | LGPL | GATE open source community |
| Gensim | Python | LGPL | Radim Řehůřek |
| LinguaStream | Java | Free for research | University of Caen, France |
| Mallet | Java | Common Public License | University of Massachusetts Amherst |
| Modular Audio Recognition Framework | Java | BSD | The MARF Research and Development Group, Concordia University |
| MontyLingua | Python, Java | Free for research | MIT |
| Natural Language Toolkit (NLTK) | Python | Apache 2 | |
| Apache OpenNLP | Java | Apache License 2 | Online community |
| spaCy | Python, Cython | MIT | Matthew Honnibal, Explosion AI |
| UIMA | Java / C++ | Apache 2 | Apache |
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- Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing