🦆 Contextually-keyed word vectors

Using pre trained word embeddings (Fasttext, Word2Vec)

Incremental learning of word embeddings with context informativeness.

Text classification with Reuters-21578 datasets using Gensim Word2Vec and Keras LSTM

Wikidata embedding

Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Network

Aspect-Based Sentiment Analysis

Deep Learning notes and practical implementation with Tensorflow and keras. Text Analytics and practical application implementation with NLTK, Spacy and Gensim.

NLP with NLTK for Sentiment analysis amazon Products Reviews

Creating word embeddings from scratch and visualize them on TensorBoard. Using trained embeddings in Keras.

Ready to use Spanish Word2Vec embeddings created from >18B chars and >3B words

A resume filtering based on natural language processing

A simple web application for searching Word2Vec embeddings derived from approximately 2,000 law reports published by the The Incorporated Council of Law Reporting for England & Wales (https://www.iclr.co.uk).

Code to run LDA algorithm on Twitter/Foursquare scraped data.

A tool to view how Word2Vec represents words in your favourite books.

Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network

BBC News classification algorithm comparison

A web service that exposes semantic similarity search via a web GUI and a RESTful API.

This repository is contains the Word2Vec model for Harry Potter series.

Incremental learning of word embeddings with context informativeness

Generate and predict text, using Recurrent Neural Networks. (Keras+Tensorflow+Gensim)

Machine Learning Practise

A Natural Language Enabled for Pharmaceutical research data. We aim to easily and efficiently find any search results using a word2vec encoder

NLP notebooks

📷 Crawl and Analyze Instagram Hashtag Data: KoNLPY to gensim word2Vec & scikit-learn TF-IDF

ArWordVec is a collection of pre-trained word embedding model built from huge repository of Arabic tweets in different topics. The aim of these models is to support the community in their Arabic NLP-based research.

Get Similarity of two sentences based gensim word2vec trained model.

Natural Language Processing (traitement automatique du langage naturel )

Automnomously attempting a categorical summarization of a sparse, asymmetrical corpus in English language, by performing text classification - which is achieved by our intuitive sentence pair classification scenarios and usecases.

Train a word2vec model to learn vector representation for words