Curated list of awesome lists
Awesome NLP with Ruby
Useful resources for text processing in Ruby
This curated list comprises awesome
resources, libraries, information sources about computational processing of texts
in human languages with the Ruby programming language.
That field is often referred to as
HLT (Human Language Technology)
and can be brought in conjunction with
and other related disciplines.
This list comes from our day to day work on Language Models and NLP Tools.
Read why this list is awesome. Our FAQ describes the
important decisions and useful answers you may be interested in.
:sparkles: Every contribution is welcome! Add links through pull
requests or create an issue to start a discussion.
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NLP Pipeline Subtasks
An NLP Pipeline starts with a plain text.
Definition framework for operation pipelines.
Spark bindings with an easy to understand DSL.
Simplified Ruby Client for Apache Kafka.
Supervisor for parallel execution on multiple CPUs or in many threads.
Rake extensions to run local and remote tasks in parallel.
Language Identification is one of the first crucial steps in every NLP Pipeline.
Language Categorization and Identification.
Tools for Tokenization, Word and Sentence Boundary Detection and Disambiguation.
Simple multilingual tokenizer.
Multilingual tokenizer to split a string into tokens.
Natural language processing algorithms implemented in pure Ruby with minimal dependencies.
Simple and customizable text tokenization library.
Word Boundary Disambiguation with many cookies.
Pure Ruby implementation of the Punkt Segmenter.
RegExp based tokenizer for different languages.
Sentence Boundary Disambiguation tool.
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 since
stems are not necessarily have
Ruby-Stemmer exposes the SnowBall API to Ruby.
Conservative stemmer for search and indexing.
Lemmatization is considered a process of finding a base form of a word. Lemmas
are often collected in dictionaries.
WordNet based Lemmatizer for English texts.
Lexical Statistics: Counting Types and Tokens
Facilities to count word occurrences in a text.
Word counter for
Pure Ruby library counting word statistics with different custom options.
Filtering Stop Words
stopwords-filter - Filter and
Stop Word Lexicon based on the SnowBall lemmatizer.
Phrasal Level Processing
Break words and phrases into ngrams.
Flexible and general-purpose ngrams library written in pure Ruby.
Set of five distance types between strings (including Levenshtein, Sellers, Jaro-Winkler, 'pair distance').
Calculates edit distance using the Damerau-Levenshtein algorithm.
Fast Ruby FFI string edit distance algorithms.
Fast string edit distance computation, using the Damerau-Levenshtein algorithm.
Term Frequency / Inverse Document Frequency in pure Ruby.
Calculate the similarity between texts using TF/IDF.
High Level Tasks
Spelling and Error Correction
Alignment routines for bilingual texts (Gale-Church implementation).
Google API Ruby Client.
Ruby client for the microsoft translator API.
Google Translate with speech synthesis in your terminal.
implementation of BLEU and other base algorithms.
Numbers, Dates, and Time Parsing
Pure Ruby natural language date parser.
Simple Ruby natural language parser for date and time ranges.
Pure Ruby parser for elapsed time.
Methods for parsing and formatting human readable dates.
Extracts date, time, and message information from naturally worded text.
Parser for recurring and repeating events.
Ruby parser for English number expressions.
Named Entity Recognition
Named Entity Recognition with Stanford NER and Ruby.
Ruby Binding for Stanford Pos-Tagger and Name Entity Recognizer.
Small Ruby API for utilizing 'espeak' and 'lame' to create text-to-speech mp3 files.
Text-to-Speech conversion using the Google translate service.
Ruby wrapper over the AT&T Speech API for speech to text.
Dialog Agents, Assistants, and Chatbots
Straightforward ruby-based Twitter Bot Framework, using OAuth to authenticate.
Highly extensible chat operation bot framework written with persistent storage on Redis.
Machine Learning Libraries
Machine Learning Algorithms
in pure Ruby or written in other programming languages with appropriate bindings
For more up-to-date list please look at the Awesome ML with Ruby list.
Support Vector Machines with Ruby.
JRuby bindings for Weka, different ML algorithms implemented through Weka.
Decision Tree ID3 Algorithm in pure Ruby
Memory based learners from the Timbl framework.
General classifier module to allow Bayesian and other types of classifications.
Ruby implementation of the LDA
(Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering.
Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).
Redis-backed Bayesian classifier.
JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.
Simple Naive Bayes classifier.
Full-featured, Ruby implementation of Naive Bayes.
Generalized rack framework for text classifications.
Naive Bayes text classification implementation as an OmniCat classifier strategy.
Ruby bindings to the Fast Artificial Neural Network Library (FANN).
rblearn - Feature Extraction and Crossvalidation library.
Please refer to the Data Visualization
section on the Data Science with Ruby list.
Optical Character Recognition
library for extracting text and metadata from files and documents
using the Apache Tika content analysis toolkit.
Full Text Search, Information Retrieval, Indexing
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 string comparison with Distance measures and Regular Expression.
Fuzzy string matching library for Ruby.
ActiveSupport gem has various string extensions that can handle case.
Toolset for fuzzy searches in Ruby tuned for accuracy.
U extends Ruby’s Unicode support.
Unicode normalization library.
Find a lot of kinds of common information in a string.
Generate strings that match a given regular expression.
Make difficult regular expressions easy.
Transliterate Hebrew & Yiddish text into Latin characters.
hight-speed Regular Expression library for Text Mining and Text Extraction.
sample string generation from a given Regular Expression.
transliteration Cyrillic to Latin in many possible ways (defined by the reference implementation).
Articles, Posts, Talks, and Presentations
Projects and Code Examples
Text Processing with Ruby: Extract Value from the Data That Surrounds You.
Pragmatic Programmers, 2015.
Scripting Intelligence: Web 3.0 Information Gathering and Processing.
Practical Semantic Web and Linked Data Applications. Lulu, 2010.
Needs your Help!
All projects in this section are really important for the community but need
more attention. Please if you have spare time and dedication spend some hours
on the code here.
Awesome NLP with Ruby by Andrei Beliankou and
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Awesome NLP with Ruby has waived all copyright and related or neighboring rights
Awesome NLP with Ruby.
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