Immersive first-person parkour in a surreal, physics-driven voxel world.

Machine-readable lists of lemma-token pairs in 23 languages.

A Python wrapper of the Yandex Mystem 3.1 morphological analyzer (http://api.yandex.ru/mystem). The original tool is shipped as a binary and this library makes it easy to integrate it in Python projects. Let us know in the issues if you would like to be involved into the developments or maintenance of this project. If you have any fix or suggestion, please make a pull request. We are very open to accepting any contributions.

A single model that parses Universal Dependencies across 75 languages. Given a sentence, jointly predicts part-of-speech tags, morphology tags, lemmas, and dependency trees.

REST'ful web framework in Agda

Lemmatizer for text in English. Inspired by Python's nltk.corpus.reader.wordnet.morphy

Elasticsearch lemmatizer for 15 languages

Remote CLI tools at your fingertips

Lemmatization for Turkish Language

This Anki deck contains top 5,000 high-frequency English lemmas (as ranked by COCA) in an English-only environment. Each atomic card presents a single sense, with expert-level definitions from Merriam-Webster’s Learner’s Dictionary and dual-track audio (native recordings + TTS) to boost both memorization and listening practice.

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.

Kan extensions, Kan lifts, the Yoneda lemma, and (co)monads generated by a functor

🤘Lemmy is a lemmatizer for Danish 🇩🇰 and Swedish 🇸🇪

comparative formalizations of the Yoneda lemma for 1-categories and infinity-categories

JavaScript Lemmatizer is a lemmatization library to retrieve a base form from an English inflected word.

English lemmatizer

A dataset mapping English words to CEFR levels based on the CEFR-J dataset, word lemmas, stems, parts of speech (POS), and frequency data from the N-Gram Google dataset. Ideal for NLP tasks, language proficiency assessment, and linguistic research.

[IJCAI2023] Your text images can be clearer!

A journal of theorems, lemmas and problems for Mathematical Olympiads.

NSLinguisticTagger provides a uniform interface to a variety of natural language processing functionality with support for many different languages and scripts. One can use this class to segment natural language text into paragraphs , sentences, or words and tag information about those segments such as parts of speech, lexical class, lemma!

A library that adds some NLP capabilities to the Lucene search engine

A Morphological Parser (Analyser) / Lemmatizer written in Elixir.

Spanish to English dictionary, frequency list, and lemma data

Note-taking app, written in Python with Gtk

Ukrainian lemmatizer plugin for ElasticSearch

A French Lemmatizer in Python based on the LEFFF

한국어 용언 분석기 (원형 복원, 용언 형태소 분석)

Mailgun Cryptographic Tools

Spanish rule-based lemmatization for spaCy

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