spaCy Ecosystem
spaCy is a free library for advanced Natural Language Processing (NLP) in Python. It’s designed specifically for production use and helps you build applications that process and “understand” large volumes of text. It can be used to build information extraction or natural language understanding systems.
Keywords
Languages
zix_understandability-index
Get a pragmatic assessment how understandable a German text is
nlp_data_visualization
This project provides Python scripts for analyzing and visualizing text data using efficient NLP methods
Text-Summarization
Here are the two NLP models for text summarization: Abstractive NLP and Extractive NLP
Intel_Sentiment_Analysis
Intel Review Analyzer is a powerful tool designed to help businesses understand customer sentiments through automated analysis of reviews
DeepLearningForCoders
Hey, this repo contains code from deep learning specialization by Andrew NG
Healthcare-Diagnosis-Chatbot-MS-Hackathon
Built a chatbot capable of diagnosing common medical conditions based on user symptoms input
NER-Model-for-Company-Names-GOST-and-Product-Names-Detect
Модель NER для определения названий компаний, стандартов госта и названий товаров с точностью 97%
Subtheme-Sentiment-Analysis-for-Review
"Comprehensive Subtheme Sentiment Analysis of Customer Reviews Using Advanced NLP Techniques"
simply-simplify-language
Use machine learning to make your institutional communication more understandable and inclusive
Talented-recruitment-and-skills-analysis-system
The project's goal is to help job seekers understand the basic qualifications for specific jobs and evaluate the suitability of their skills for those positions
Breakthrough-HIRE
The project aims to help job seekers understand the essential qualifications required for specific jobs and assess how well their skills match those positions
NLP-Notebooks
Explore NLP tasks with Python using NLTK, SpaCy & scikit-learn: Tokenization, Normalization, NER, POS tagging, Encoding, Word embedding
lftk
[BEA @ ACL 2023] General-purpose tool for linguistic features extraction; Tested on readability assessment, essay scoring, fake news detection, hate speech detection, etc
