Tools and resources for the computational processing of Nheengatu (Modern Tupi)

Korean NLP Python Library for Economic Analysis

State-of-the-art neural tagger and lemmatizer for ancient languages

OpenNLP models generator for various languages

Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. All NLP modules are based on Timbl, the Tilburg memory-based learning software package.

Machine Learning approach to Bengali Corpus POS (Parts of Speech) Tagging using BNLP (Bengali Natural Language Processing) Toolkit. This is the Minor Project Presentation at Heritage Institute of Technology under the mentorship of Prof. Sandipan Ganguly.

HTTP wrapper of the VnCoreNLP library - A Vietnamese natural language processing toolkit

UDAR Does Accented Russian: A finite-state morphological analyzer of Russian that handles stressed wordforms.

Part of speech tagging of words and Named-entity recognition in Russian language using deep neural network in C# for .NET

API of Articut 中文斷詞 (兼具語意詞性標記)：「斷詞」又稱「分詞」，是中文資訊處理的基礎。Articut 不用機器學習，不需資料模型，只用現代白話中文語法規則，即能達到 SIGHAN 2005 F1-measure 94% 以上，Recall 96% 以上的成績。

Polish morphosyntactic tagger.

HuSpaCy: industrial-strength Hungarian natural language processing

Определение частей речи / Нормализация текста: приведение всех слов к словарной форме в тексте на русском языке

Part-of-Speech Tagging / Normalization of words in English texts

A part-of-speech/morphological tagger and a dependency parser that can be trained separately or jointly, with special methods for Ancient Greek.

Juman++ (a Morphological Analyzer Toolkit)

This repo contains a Python script called get_linguistic_features.py - an information extraction script which performs part-of-speech (PoS) tagging and named-entity recognition (NER).

Perceptron model from scratch for part-of-speech prediction.

Train and evaluate neural network language models for POS tagging, tag input sentences according to a trained model.

An Indonesian NLP tool that conforms to Universal Dependencies v2 annotation guidelines