Natural Language Toolkit

Software suite for natural language processing

Natural Language Toolkit

Original author(s)| Steven Bird, Edward Loper, Ewan Klein
Developer(s)| Team NLTK
Initial release| 2001; 24 years ago (2001)[1]
Stable release|

3.9.1[2]  / 19 August 2024; 11 months ago (19 August 2024)

Repository|

* github.com/nltk/nltk

Written in| Python
Type| Natural language processing
License| Apache 2.0[3]
Website| www.nltk.org
Parse tree generated with NLTK

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] It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania.[5] NLTK includes graphical demonstrations and sample data. 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 machine learning.[8] NLTK has been used successfully as a teaching tool, as an individual study tool, and as a platform for prototyping and building research systems. There are 32 universities in the US and 25 countries using NLTK in their courses.

## Library highlights

[edit]

* Discourse representation[9]
* Lexical analysis: Word and text tokenizer
* n-gram and collocations
* Part-of-speech tagger
* Tree model and Text chunker for capturing
* Named-entity recognition

## See also

[edit]

* Free and open-source software portal

* SpaCy

## References

[edit]

1. ^ "Project site on SourceForge". 9 July 2001.
2. ^ "Release 3.9.1". 19 August 2024. Retrieved 23 August 2024.
3. ^ "NLTK License". NLTK Project. Retrieved 14 February 2015.
4. ^ "NLTK Courses". Google Docs. Retrieved 15 June 2016.
5. ^ "Preface". www.nltk.org. Retrieved 15 June 2016.
6. ^ Bird, Steven; Klein, Ewan; Loper, Edward (2009). Natural Language Processing with Python. O'Reilly Media Inc. ISBN 978-0-596-51649-9.
7. ^ Perkins, Jacob (2010). Python Text Processing with NLTK 2.0 Cookbook. Packt Publishing. ISBN 978-1849513609.
8. ^ Bird, Steven; Klein, Ewan; Loper, Edward; Baldridge, Jason (2008). "Multidisciplinary instruction with the Natural Language Toolkit" (PDF). Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics, ACL. Archived from the original (PDF) on 2 September 2011. `{{cite journal}}`: Cite journal requires `|journal=` (help)
9. ^ "NLTK :: Sample usage for drt". www.nltk.org. Retrieved 14 July 2022.

## External links

[edit]

* Official website

* v
* t
* e

Natural language processing

General terms|

* AI-complete
* Bag-of-words
* n-gram
* Bigram
* Trigram
* Computational linguistics
* Natural language understanding
* Stop words
* Text processing

Text analysis|

* Argument mining
* Collocation extraction
* Concept mining
* Coreference resolution
* Deep linguistic processing
* Distant reading
* Information extraction
* Named-entity recognition
* Ontology learning
* Parsing
* Semantic parsing
* Syntactic parsing
* Part-of-speech tagging
* Semantic analysis
* Semantic role labeling
* Semantic decomposition
* Semantic similarity
* Sentiment analysis

* Terminology extraction
* Text mining
* Textual entailment
* Truecasing
* Word-sense disambiguation
* Word-sense induction

| Text segmentation|

* Compound-term processing
* Lemmatisation
* Lexical analysis
* Text chunking
* Stemming
* Sentence segmentation
* Word segmentation

|

Automatic summarization|

* Multi-document summarization
* Sentence extraction
* Text simplification

Machine translation|

* Computer-assisted
* Example-based
* Rule-based
* Statistical
* Transfer-based
* Neural

Distributional semantics models|

* BERT
* Document-term matrix
* Explicit semantic analysis
* fastText
* GloVe
* Language model (large)
* Latent semantic analysis
* Seq2seq
* Word embedding
* Word2vec

Language resources,
datasets and corpora|

| Types and
standards|

* Corpus linguistics
* Lexical resource
* Linguistic Linked Open Data
* Machine-readable dictionary
* Parallel text
* PropBank
* Semantic network
* Simple Knowledge Organization System
* Speech corpus
* Text corpus
* Thesaurus (information retrieval)
* Treebank
* Universal Dependencies

|

Data|

* BabelNet
* Bank of English
* DBpedia
* FrameNet
* Google Ngram Viewer
* UBY
* WordNet
* Wikidata

Automatic identification
and data capture|

* Speech recognition
* Speech segmentation
* Speech synthesis
* Natural language generation
* Optical character recognition

Topic model|

* Document classification
* Latent Dirichlet allocation
* Pachinko allocation

Computer-assisted
reviewing|

* Automated essay scoring
* Concordancer
* Grammar checker
* Predictive text
* Pronunciation assessment
* Spell checker

Natural language
user interface|

* Chatbot
* Interactive fiction
* Question answering
* Virtual assistant
* Voice user interface

Related|

* Formal semantics
* Hallucination
* Natural Language Toolkit
* spaCy

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