Suggest edit — nlp-with-ruby

Title

Name

Note

---
visibility: public
---

# NLP with Ruby

**repo:** [arbox/nlp-with-ruby](https://github.com/arbox/nlp-with-ruby)  
**category:** [[computer-science|Computer Science]]
**related:** [[ruby|Ruby]]

---

[](https://www.patreon.com/arbox)

[[RubyML](https://github.com/arbox/machine-learning-with-ruby) |
 [RubyDataScience](https://github.com/arbox/data-science-with-ruby) |
 [RubyInterop](https://github.com/arbox/ruby-interoperability)]

# Awesome NLP with Ruby [<img src="ruby.jpg" align="left" width="30px" height="30px" />][ruby]

> Useful resources for text processing in Ruby

This curated list comprises [_awesome_](https://github.com/sindresorhus/awesome/blob/master/awesome.md)
resources, libraries, information sources about computational processing of texts
in human languages with the [Ruby programming language](ruby).
That field is often referred to as
[NLP](https://en.wikipedia.org/wiki/Natural_language_processing),
[Computational Linguistics](https://en.wikipedia.org/wiki/Computational_linguistics),
[HLT](https://en.wikipedia.org/wiki/Language_technology) (Human Language Technology)
and can be brought in conjunction with
[Artificial Intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence),
[Machine Learning](https://en.wikipedia.org/wiki/Machine_learning),
[Information Retrieval](https://en.wikipedia.org/wiki/Information_retrieval),
[Text Mining](https://en.wikipedia.org/wiki/Text_mining),
[Knowledge Extraction](https://en.wikipedia.org/wiki/Knowledge_extraction)
and other related disciplines.

This list comes from our day to day work on Language Models and NLP Tools.
Read [why](motivation.md) this list is awesome. Our [FAQ](FAQ.md) describes the
important decisions and useful answers you may be interested in.

:sparkles: Every [contribution](#contributing) is welcome! Add links through pull
requests or create an issue to start a discussion.

## Contents

- [:sparkles: Tutorials](#sparkles-tutorials)
- [NLP Pipeline Subtasks](#nlp-pipeline-subtasks)
  * [Pipeline Generation](#pipeline-generation)
  * [Multipurpose Engines](#multipurpose-engines)
    + [On-line APIs](#on-line-apis)
  * [Language Identification](#language-identification)
  * [Segmentation](#segmentation)
  * [Lexical Processing](#lexical-processing)
    + [Stemming](#stemming)
    + [Lemmatization](#lemmatization)
    + [Lexical Statistics: Counting Types and Tokens](#lexical-statistics-counting-types-and-tokens)
    + [Filtering Stop Words](#filtering-stop-words)
  * [Phrasal Level Processing](#phrasal-level-processing)
  * [Syntactic Processing](#syntactic-processing)
    + [Constituency Parsing](#constituency-parsing)
  * [Semantic Analysis](#semantic-analysis)
  * [Pragmatical Analysis](#pragmatical-analysis)
- [High Level Tasks](#high-level-tasks)
  * [Spelling and Error Correction](#spelling-and-error-correction)
  * [Text Alignment](#text-alignment)
  * [Machine Translation](#machine-translation)
  * [Sentiment Analysis](#sentiment-analysis)
  * [Numbers, Dates, and Time Parsing](#numbers-dates-and-time-parsing)
  * [Named Entity Recognition](#named-entity-recognition)
  * [Text-to-Speech-to-Text](#text-to-speech-to-text)
- [Dialog Agents, Assistants, and Chatbots](#dialog-agents-assistants-and-chatbots)
- [Linguistic Resources](#linguistic-resources)
- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Libraries](#machine-learning-libraries)
- [Data Visualization](#data-visualization)
- [Optical Character Recognition](#optical-character-recognition)
- [Text Extraction](#text-extraction)
- [Full Text Search, [Information Retrieval](/@harrisonqian/awesome/wiki/computer-science/information-retrieval), Indexing](#full-text-search-information-retrieval-indexing)
- [Language Aware String Manipulation](#language-aware-string-manipulation)
- [Articles, Posts, [Talks](/@harrisonqian/awesome/wiki/theory/talks), and Presentations](#articles-posts-talks-and-presentations)
- [Projects and Code Examples](#projects-and-code-examples)
- [Books](#books)
- [Community](#community)
- [Needs your Help!](#needs-your-help)
- [Related Resources](#related-resources)
- [License](#license)

## :sparkles: Tutorials

Please help us to fill out this section! :smiley:

## NLP Pipeline Subtasks

An NLP Pipeline starts with a plain text.

### Pipeline Generation

- [composable_operations](https://github.com/t6d/composable_operations) -
  Definition framework for operation pipelines.
- [ruby-spark](https://github.com/ondra-m/ruby-spark) -
  Spark bindings with an easy to understand DSL.
- [phobos](https://github.com/phobos/phobos) -
  Simplified Ruby Client for [Apache Kafka](https://kafka.apache.org/).
- [parallel](https://github.com/grosser/parallel) -
  Supervisor for parallel execution on multiple CPUs or in many threads.
- [pwrake](https://github.com/masa16/pwrake) -
  Rake extensions to run local and remote tasks in parallel.

### Multipurpose Engines

- [open-nlp](https://github.com/louismullie/open-nlp) -
  Ruby Bindings for the [OpenNLP](https://opennlp.apache.org/) Toolkit.
- [stanford-[core](/@harrisonqian/awesome/wiki/platforms/core)-nlp](https://github.com/louismullie/stanford-core-nlp) -
  Ruby Bindings for the Stanford [CoreNLP](https://github.com/stanfordnlp/CoreNLP) tools.
- [treat](https://github.com/louismullie/treat) -
  Natural Language Processing framework for Ruby (like [NLTK](http://www.nltk.org/) for Python).
- [nlp_toolz](https://github.com/LeFnord/nlp_toolz) -
  Wrapper over some [OpenNLP](https://opennlp.apache.org/) classes and
  the original [Berkeley Parser](https://github.com/slavpetrov/berkeleyparser).
- [open_nlp](https://github.com/hck/open_nlp) -
  JRuby Bindings for the [OpenNLP](https://opennlp.apache.org/) Toolkit.
- [ruby-spacy](https://github.com/yohasebe/ruby-spacy) &mdash;
  Wrapper module for spaCy NLP library via [PyCall](https://github.com/mrkn/pycall.rb).

#### On-line APIs

- [alchemyapi_ruby](https://github.com/alchemyapi/alchemyapi_ruby) -
  Legacy Ruby SDK for AlchemyAPI/Bluemix.
- [wit-ruby](https://github.com/wit-ai/wit-ruby) -
  Ruby client library for the [Wit.ai](https://wit.ai/) Language Understanding Platform.
- [wlapi](https://github.com/arbox/wlapi) - [Ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby) client library for
  [Wortschatz Leipzig](http://wortschatz.uni-leipzig.de/de) web services.
- [monkeylearn-ruby](https://github.com/monkeylearn/monkeylearn-ruby) - Sentiment
  Analysis, Topic Modelling, Language Detection, Named Entity Recognition via
  a Ruby based Web API client.
- [google-cloud-language](https://github.com/googleapis/google-cloud-ruby/tree/master/google-cloud-language) -
  Google's Natural Language service API for Ruby.

### Language Identification

Language Identification is one of the first crucial steps in every NLP Pipeline.

- [scylla](https://github.com/hashwin/scylla) -
  Language Categorization and Identification.

### Segmentation

Tools for Tokenization, Word and Sentence Boundary Detection and Disambiguation.

- [tokenizer](https://github.com/arbox/tokenizer) -
 Simple multilingual tokenizer.
 [[tutorial](tutorials/tokenizer.md)]
- [pragmatic_tokenizer](https://github.com/diasks2/pragmatic_tokenizer) -
 Multilingual tokenizer to split a string into tokens.
- [nlp-pure](https://github.com/parhamr/nlp-pure) -
 Natural language processing algorithms implemented in pure Ruby with minimal dependencies.
- [textoken](https://github.com/manorie/textoken) -
 Simple and customizable text tokenization library.
- [pragmatic_segmenter](https://github.com/diasks2/pragmatic_segmenter) -
 Word Boundary Disambiguation with many cookies.
- [punkt-segmenter](https://github.com/lfcipriani/punkt-segmenter) -
 Pure Ruby implementation of the Punkt Segmenter.
- [tactful_tokenizer](https://github.com/zencephalon/Tactful_Tokenizer) -
 RegExp based tokenizer for different languages.
- [scapel](https://github.com/louismullie/scalpel) -
 Sentence Boundary Disambiguation tool.

### Lexical Processing

#### Stemming

Stemming is the term used in information retrieval to describe the process for
reducing wordforms to some base representation. Stemming should be distinguished
from [Lemmatization](#lemmatization) since `stems` are not necessarily have
linguistic motivation.

- [ruby-stemmer](https://github.com/aurelian/ruby-stemmer) -
  Ruby-Stemmer exposes the SnowBall API to Ruby.
- [uea-stemmer](https://github.com/ealdent/uea-stemmer) -
  Conservative stemmer for search and indexing.

#### Lemmatization

Lemmatization is considered a process of finding a base form of a word. Lemmas
are often collected in dictionaries.

- [lemmatizer](https://github.com/yohasebe/lemmatizer) -
  WordNet based Lemmatizer for English texts.

#### Lexical Statistics: Counting Types and Tokens

- [wc](https://github.com/thesp0nge/wc) -
  Facilities to count word occurrences in a text.
- [word_count](https://github.com/AtelierConvivialite/word_count) -
  Word counter for `String` and `Hash` objects.
- [words_counted](https://github.com/abitdodgy/words_counted) -
  Pure Ruby library counting word statistics with different custom options.

#### Filtering Stop Words

- [stopwords-filter](https://github.com/brenes/stopwords-filter) - Filter and
  Stop Word Lexicon based on the SnowBall lemmatizer.

### Phrasal Level Processing

- [n_gram](https://github.com/reddavis/N-Gram) -
  N-Gram generator.
- [ruby-ngram](https://github.com/tkellen/ruby-ngram) -
  Break words and phrases into ngrams.
- [raingrams](https://github.com/postmodern/raingrams) -
  Flexible and general-purpose ngrams library written in pure Ruby.

### Syntactic Processing

#### Constituency Parsing

- [stanfordparser](https://rubygems.org/gems/stanfordparser) -
 Ruby based wrapper for the Stanford Parser.
- [rley](https://github.com/famished-tiger/Rley) -
 Pure Ruby implementation of the [Earley](https://en.wikipedia.org/wiki/Earley_parser)
 Parsing Algorithm for Context-Free Constituency Grammars.
- [rsyntaxtree](https://github.com/yohasebe/rsyntaxtree) -
 Visualization for syntactic trees in Ruby based on [RMagick](https://github.com/rmagick/rmagick).
 [dep: [ImageMagick](#imagemagick)]

### Semantic Analysis

- [amatch](https://github.com/flori/amatch) -
  Set of five distance types between strings (including Levenshtein, Sellers, Jaro-Winkler, 'pair distance').
- [damerau-levenshtein](https://github.com/GlobalNamesArchitecture/damerau-levenshtein) -
  Calculates edit distance using the Damerau-Levenshtein algorithm.
- [hotwater](https://github.com/colinsurprenant/hotwater) -
  Fast Ruby FFI string edit distance algorithms.
- [levenshtein-ffi](https://github.com/dbalatero/levenshtein-ffi) -
  Fast string edit distance computation, using the Damerau-Levenshtein algorithm.
- [tf_idf](https://github.com/reddavis/TF-IDF) -
  Term Frequency / Inverse Document Frequency in pure Ruby.
- [tf-idf-similarity](https://github.com/jpmckinney/tf-idf-similarity) -
  Calculate the similarity between texts using TF/IDF.

### Pragmatical Analysis
- [SentimentLib](https://github.com/nzaillian/sentiment_lib) -
  Simple extensible sentiment analysis gem.

## High Level Tasks

### Spelling and Error Correction

- [gingerice](https://github.com/subosito/gingerice) -
  Spelling and Grammar corrections via the [Ginger](https://www.gingersoftware.com/) API.
- [hunspell-i18n](https://github.com/romanbsd/hunspell) -
  Ruby bindings to the standard [Hunspell](https://hunspell.github.io/) Spell Checker.
- [ffi-hunspell](https://github.com/postmodern/ffi-hunspell) -
  FFI based Ruby bindings for [Hunspell](https://hunspell.github.io/).
- [hunspell](https://github.com/segabor/Hunspell) -
  Ruby bindings to [Hunspell](https://hunspell.github.io/) via Ruby C API.

### Text Alignment

- [alignment](https://github.com/povilasjurcys/alignment) -
  Alignment routines for bilingual texts (Gale-Church implementation).

### Machine Translation

- [google-api-client](https://github.com/googleapis/google-api-[ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby)-client) -
  Google API Ruby Client.
- [microsoft_translator](https://github.com/ikayzo/microsoft_translator) -
  Ruby client for the microsoft translator API.
- [termit](https://github.com/pawurb/termit) -
  Google Translate with speech synthesis in your terminal.
- [zipf](https://github.com/pks/zipf) -
  implementation of BLEU and other base algorithms.

### Sentiment Analysis

- [stimmung](https://github.com/pachacamac/stimmung) -
  Semantic Polarity based on the
  [SentiWS](http://wortschatz.uni-leipzig.de/en/download) lexicon.

### Numbers, Dates, and Time Parsing

- [chronic](https://github.com/mojombo/chronic) -
  Pure Ruby natural language date parser.
- [chronic_between](https://github.com/jrobertson/chronic_between) -
  Simple Ruby natural language parser for date and time ranges.
- [chronic_duration](https://github.com/henrypoydar/chronic_duration) -
  Pure Ruby parser for elapsed time.
- [kronic](https://github.com/xaviershay/kronic) -
  Methods for parsing and formatting human readable dates.
- [nickel](https://github.com/iainbeeston/nickel) -
  Extracts date, time, and message information from naturally worded text.
- [tickle](https://github.com/yb66/tickle) -
  Parser for recurring and repeating events.
- [numerizer](https://github.com/jduff/numerizer) -
  Ruby parser for English number expressions.

### Named Entity Recognition

- [ruby-ner](https://github.com/mblongii/ruby-ner) -
  Named Entity Recognition with Stanford NER and Ruby.
- [ruby-nlp](https://github.com/tiendung/ruby-nlp) -
  Ruby Binding for Stanford Pos-Tagger and Name Entity Recognizer.

### Text-to-Speech-to-Text

- [espeak-ruby](https://github.com/dejan/espeak-ruby) -
  Small Ruby API for utilizing 'espeak' and 'lame' to create text-to-speech mp3 files.
- [tts](https://github.com/c2h2/tts) -
  Text-to-Speech conversion using the Google translate service.
- [att_speech](https://github.com/adhearsion/att_speech) -
  Ruby wrapper over the AT&T Speech API for speech to text.
- [pocketsphinx-ruby](https://github.com/watsonbox/pocketsphinx-ruby) -
  Pocketsphinx bindings.

## Dialog Agents, Assistants, and Chatbots

- [chatterbot](https://github.com/muffinista/chatterbot) -
  Straightforward ruby-based Twitter Bot Framework, using OAuth to authenticate.
- [lita](https://github.com/litaio/lita) -
  Highly extensible chat operation bot framework written with persistent storage on [Redis](https://redis.io/).

## Linguistic Resources

- [rwordnet](https://github.com/doches/rwordnet) -
  Pure Ruby self contained API library for the [Princeton WordNet®](https://wordnet.princeton.edu/).
- [wordnet](https://github.com/ged/ruby-wordnet/blob/master/README.rdoc) -
  Performance tuned bindings for the [Princeton WordNet®](https://wordnet.princeton.edu/).

## Machine Learning Libraries

[Machine Learning](https://en.wikipedia.org/wiki/Machine_learning) Algorithms
in pure Ruby or written in other programming languages with appropriate bindings
for Ruby.

For more up-to-date list please look at the [Awesome ML with Ruby][ml-with-ruby] list.

- [rb-libsvm](https://github.com/febeling/rb-libsvm) -
 Support Vector Machines with Ruby.
- [weka](https://github.com/paulgoetze/weka-jruby) -
 JRuby bindings for Weka, different ML algorithms implemented through Weka.
- [decisiontree](https://github.com/igrigorik/decisiontree) -
 Decision Tree ID3 Algorithm in pure Ruby
 [[post](https://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/)].
- [rtimbl](https://github.com/maspwr/rtimbl) -
 Memory based learners from the Timbl framework.
- [classifier-reborn](https://github.com/jekyll/classifier-reborn) -
 General classifier module to allow Bayesian and other types of classifications.
- [lda-ruby](https://github.com/ealdent/lda-ruby) -
 Ruby implementation of the [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)
 (Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering.
- [liblinear-[ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby)-swig](https://github.com/tomz/liblinear-ruby-swig) -
 Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).
- [linnaeus](https://github.com/djcp/linnaeus) -
 Redis-backed Bayesian classifier.
- [maxent_string_classifier](https://github.com/mccraigmccraig/maxent_string_classifier) -
 JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.
- [naive_bayes](https://github.com/reddavis/Naive-Bayes) -
 Simple Naive Bayes classifier.
- [nbayes](https://github.com/oasic/nbayes) -
 Full-featured, Ruby implementation of Naive Bayes.
- [omnicat](https://github.com/mustafaturan/omnicat) -
 Generalized rack framework for text classifications.
- [omnicat-bayes](https://github.com/mustafaturan/omnicat-bayes) -
 Naive Bayes text classification implementation as an OmniCat classifier strategy.
- [ruby-fann](https://github.com/tangledpath/ruby-fann) -
 Ruby bindings to the [Fast Artificial Neural Network Library (FANN)](http://leenissen.dk/fann/wp/).
- [rblearn](https://github.com/himkt/rblearn) - Feature Extraction and Crossvalidation library.

## Data Visualization

Please refer to the [Data Visualization](https://github.com/arbox/data-science-with-ruby#visualization)
section on the [Data Science with Ruby][ds-with-ruby] list.

## Optical Character Recognition

* [tesseract-ocr](https://github.com/meh/ruby-tesseract-ocr) -
  FFI based wrapper over the [Tesseract OCR Engine](https://github.com/tesseract-ocr/tesseract).

## Text Extraction

- [yomu](https://github.com/yomurb/yomu) -
  library for extracting text and metadata from files and documents
  using the [Apache Tika](https://tika.apache.org/) content analysis toolkit.

## Full Text Search, Information Retrieval, Indexing

- [rsolr](https://github.com/rsolr/rsolr) -
  Ruby and Rails client library for [Apache Solr](http://lucene.apache.org/solr/).
- [sunspot](https://github.com/sunspot/sunspot) -
  Rails centric client for [Apache Solr](http://lucene.apache.org/solr/).
- [thinking-sphinx](https://github.com/pat/thinking-sphinx) -
  [Active Record](https://guides.rubyonrails.org/active_record_basics.html)
  plugin for using [Sphinx](http://sphinxsearch.com/) in (not only) Rails based projects.
- [elasticsearch](https://github.com/elastic/elasticsearch-ruby/tree/master/elasticsearch) -
  Ruby client and API for [Elasticsearch](https://www.elastic.co/).
- [elasticsearch-rails](https://github.com/elastic/elasticsearch-rails) -
  Ruby and Rails integrations for [Elasticsearch](https://www.elastic.co/).
- [google-api-client](https://github.com/googleapis/google-api-[ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby)-client) -
  Ruby API library for [Google](https://developers.google.com/api-client-library/ruby/) services.

## Language Aware String Manipulation

Libraries for language aware string manipulation, i.e. search, pattern matching,
case conversion, transcoding, regular expressions which need information about
the underlying language.

- [fuzzy_match](https://github.com/seamusabshere/fuzzy_match) -
  Fuzzy string comparison with Distance measures and Regular Expression.
- [fuzzy-string-match](https://github.com/kiyoka/fuzzy-string-match) -
  Fuzzy string matching library for Ruby.
- [active_support](https://github.com/rails/rails/tree/master/activesupport/lib/active_support) -
  RoR `ActiveSupport` gem has various string extensions that can handle case.
- [fuzzy_tools](https://github.com/brianhempel/fuzzy_tools) -
  Toolset for fuzzy searches in Ruby tuned for accuracy.
- [u](http://disu.se/software/u-1.0/) -
  U extends Ruby’s Unicode support.
- [unicode](https://github.com/blackwinter/unicode) -
  Unicode normalization library.
- [CommonRegexRuby](https://github.com/talyssonoc/CommonRegexRuby) -
  Find a lot of kinds of common information in a string.
- [regexp-examples](https://github.com/tom-lord/regexp-examples) -
  Generate strings that match a given regular expression.
- [verbal_expressions](https://github.com/ryan-endacott/verbal_expressions) -
  Make difficult regular expressions easy.
- [translit_kit](https://github.com/AnalyzePlatypus/TranslitKit) -
  Transliterate Hebrew & Yiddish text into Latin characters.
- [re2](https://github.com/mudge/re2) -
  hight-speed Regular Expression library for Text Mining and Text Extraction.
- [regex_sample](https://github.com/mochizukikotaro/regex_sample) -
  sample string generation from a given Regular Expression.
- [iuliia](https://github.com/adnikiforov/iuliia-rb) &mdash;
  transliteration Cyrillic to Latin in many possible ways (defined by the [reference implementation](https://github.com/nalgeon/iuliia)).

## Articles, Posts, Talks, and Presentations

- 2019
 - _Extracting Text From Images Using Ruby_ by [aonemd](https://twitter.com/aonemd)
 [[post](https://aonemd.github.io/blog/extracting-text-from-images-using-ruby) |
 [code](https://gist.github.com/aonemd/7bb3c4760d9e47a9ce8e270198cb40a0)]
- 2018
 - _Natural Language Processing and Tweet Sentiment Analysis_ by [Cassandra Corrales](https://twitter.com/casita305)
 [[post](https://medium.com/@cmcorrales3/natural-language-processing-and-tweet-sentiment-analysis-fa1edbb5ddd5)]
- 2017
 - _The Google NLP API Meets Ruby_ by [Aja Hammerly](https://twitter.com/the_thagomizer)
 [[post](http://www.thagomizer.com/blog/2017/04/13/the-google-nlp-api-meets-ruby.html)]
 - _Syntax Isn't Everything: NLP For Rubyists_ by [Aja Hammerly](https://twitter.com/the_thagomizer)
 [[slides](http://www.thagomizer.com/files/NLP_RailsConf2017.pdf)]
 - _Scientific Computing on JRuby_ by [Prasun Anand](https://twitter.com/prasun_anand)
 [[slides](https://www.slideshare.net/PrasunAnand2/fosdem2017-scientific-computing-on-jruby) |
 [video](https://ftp.fau.de/fosdem/2017/K.4.201/ruby_scientific_computing_on_jruby.mp4) |
 [slides](https://www.slideshare.net/PrasunAnand2/scientific-computing-on-jruby) |
 [slides](https://www.slideshare.net/PrasunAnand2/scientific-computation-on-jruby)]
 - _Unicode Normalization in Ruby_ by [Starr Horne](https://twitter.com/starrhorne)
 [[post](https://blog.honeybadger.io/ruby_unicode_normalization/)]
- 2016
 - _Quickly Create a Telegram Bot in Ruby_ by [Ardian Haxha](https://twitter.com/ArdianHaxha)
 [[tutorial](https://www.sitepoint.com/quickly-create-a-telegram-bot-in-ruby/)]
 - _Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning): An Introduction for [Ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby) Developers_ by [Geoffrey Litt](https://twitter.com/geoffreylitt)
 [[slides](https://speakerdeck.com/geoffreylitt/deep-learning-an-introduction-for-ruby-developers)]
 - _How I made a pure-[Ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby) word2vec program more than 3x faster_ by [Kei Sawada](https://twitter.com/remore)
 [[slides](https://speakerdeck.com/remore/how-i-made-a-pure-ruby-word2vec-program-more-than-3x-faster)]
 - _Dōmo arigatō, Mr. Roboto: [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) with Ruby_ by [Eric Weinstein](https://twitter.com/ericqweinstein)
 [[slides](https://speakerdeck.com/ericqweinstein/domo-arigato-mr-roboto-machine-learning-with-ruby) | [video](https://www.youtube.com/watch?v=T1nFQ49TyeA)]
- 2015
 - _N-gram Analysis for Fun and Profit_ by [Jesus Castello](https://github.com/matugm)
 [[tutorial](https://www.rubyguides.com/2015/09/ngram-analysis-ruby/)]
 - _Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) made simple with Ruby_ by [Lorenzo Masini](https://github.com/rugginoso)
 [[tutorial](https://www.leanpanda.com/blog/2015/08/24/machine-learning-automatic-classification/)]
 - _Using [Ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby) [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) to Find Paris Hilton Quotes_ by [Rick Carlino](https://github.com/RickCarlino)
 [[tutorial](http://web.archive.org/web/20160414072324/http://datamelon.io/blog/2015/using-ruby-machine-learning-id-paris-hilton-quotes.html)]
 - _Exploring Natural Language Processing in Ruby_ by [Kevin Dias](https://github.com/diasks2)
 [[slides](https://www.slideshare.net/diasks2/exploring-natural-language-processing-in-ruby)]
 - _Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) made simple with Ruby_ by [Lorenzo Masini](https://twitter.com/rugginoso)
 [[post](https://www.leanpanda.com/blog/2015/08/24/machine-learning-automatic-classification/)]
 - _Practical [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) in Ruby_ by Bobby Grayson
 [[slides](http://slides.com/bobbygrayson/p#/)]
- 2014
 - _Natural Language Parsing with Ruby_ by [Glauco Custódio](https://github.com/glaucocustodio)
 [[tutorial](http://glaucocustodio.github.io/2014/11/10/natural-language-parsing-with-ruby/)]
 - _Demystifying [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science): Analyzing Conference [Talks](/@harrisonqian/awesome/wiki/theory/talks) with [Rails](/@harrisonqian/awesome/wiki/back-end-development/rails) and Ngrams_ by
 [Todd Schneider](https://github.com/toddwschneider)
 [[video](https://www.youtube.com/watch?v=2ZDCxwB29Bg) | [code](https://github.com/Genius/abstractogram)]
 - _Natural Language Processing with Ruby_ by [Konstantin Tennhard](https://github.com/t6d)
 [[video](https://www.youtube.com/watch?v=5u86qVh8r0M) | [video](https://www.youtube.com/watch?v=oFmy_QBQ5DU) |
 [video](https://www.youtube.com/watch?v=sPkeeWnsMn0) |
 [slides](http://euruko2013.org/speakers/presentations/natural_language_processing_with_ruby_and_opennlp-tennhard.pdf)]
- 2013
 - _How to parse 'go' - Natural Language Processing in Ruby_ by
 [Tom Cartwright](https://twitter.com/tomcartwrightuk)
 [[slides](https://www.slideshare.net/TomCartwright/natual-language-processing-in-ruby) |
 [video](https://skillsmatter.com/skillscasts/4883-how-to-parse-go)]
 - _Natural Language Processing in Ruby_ by [Brandon Black](https://twitter.com/brandonmblack)
 [[slides](https://speakerdeck.com/brandonblack/natural-language-processing-in-ruby) |
 [video](http://confreaks.tv/videos/railsconf2013-natural-language-processing-with-ruby)]
 - _Natural Language Processing with [Ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby): n-grams_ by [Nathan Kleyn](https://github.com/nathankleyn)
 [[tutorial](https://www.sitepoint.com/natural-language-processing-ruby-n-grams/) |
 [code](https://github.com/nathankleyn/ruby-nlp)]
 - _Seeking Lovecraft, Part 1: An introduction to NLP and the Treat Gem_ by
 [Robert Qualls](https://github.com/rlqualls)
 [[tutorial](https://www.sitepoint.com/seeking-lovecraft-part-1-an-introduction-to-nlp-and-the-treat-gem/)]
- 2012
 - _Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) with [Ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby), Part One_ by [Vasily Vasinov](https://twitter.com/vasinov)
 [[tutorial](http://www.vasinov.com/blog/machine-learning-with-ruby-part-one/)]
- 2011
 - _Ruby one-liners_ by [Benoit Hamelin](https://twitter.com/benoithamelin)
 [[post](http://benoithamelin.tumblr.com/ruby1line)]
 - _Clustering in Ruby_ by [Colin Drake](https://twitter.com/colinfdrake)
 [[post](https://colindrake.me/post/k-means-clustering-in-ruby/)/)]
- 2010

---
*truncated — [full list on GitHub](https://github.com/arbox/nlp-with-ruby)*