Doctrine Inflector is a small library that can perform string manipulations with regard to uppercase/lowercase and singular/plural forms of words.

Converts words between their singular and plural forms (English only)

Correctly generate plurals, ordinals, indefinite articles; convert numbers to words

A morphological solution for Russian and English language written completely in PHP. Provides classes to inflect personal names, geographical names, decline and pluralize nouns, generate cardinal and ordinal numerals, spell out money amounts and time intervals. / Морфологическая библиотека для английского и русского языков. Склоняет имена собственные, названия географических объектов, склонение и плюрализация имен собственных и другое.

A port of inflection-js to node.js module

Pluralizes and singularizes English nouns

A port of Ruby on Rails' inflector to Python

Efficiently Singularize and Pluralize Strings

Petrovich, an inflector for Russian anthroponyms.

An Inflector library for Elixir

📦 English inflection library for noun (plural to singular and singular to plural), verb (gerund, present & past) and adjectives (comparative & superlative) transformations/conjugation.

Singular to plural english word converter

JS library to inflect Russian first, last, and middle names.

A python module for English lemmatization and inflection.

underscore -> UPCASE -> CamelCase conversion of names

custom inflections for nodejs

Rails-like inflection library for Clojure and ClojureScript

A multilingual inflector that transforms words from singular to plural, underscore to camel case, and formats strings in various ways.

No description available

A port of ActiveSupport's inflector to Node.js and the browser.

ActiveSupport::Inflector, for underscore!

A rust inflection library

[READ-ONLY] CakePHP Utility classes such as Inflector, Text, Hash, Security and Xml. This repo is a split of the main code that can be found in https://github.com/cakephp/cakephp

ember-inflector goal is to be rails compatible.

Inflector for Ruby

A python module for word inflections designed for use with spaCy.

Pybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.

:es: Sane and multilingual singularization/pluralization rules for ActiveSupport and Rails.

Simplifies the graphile-build-pg inflector to trim the `ByFooIdAndBarId` from relations

An Objective-C implementation of ActiveSupport's Inflector