Unofficial implementation of multimodal skip-gram model [Lazaridou+ 2015]
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Updated
May 5, 2019 - Jupyter Notebook
Unofficial implementation of multimodal skip-gram model [Lazaridou+ 2015]
Implementation for word2vec using skip-gram architecture and negative sampling.
Queen's University - Data Mining (CISC 873)
A Search engine to retrieve text documents in response to the user's Query.
Extra tools for working with word embeddings, such as those in Embeddings.jl. However, the compatibility is currently limited.
We have implemented, expanded and reviewed the paper “Sense2Vec - A Fast and Accurate Method For Word Sense Disambiguation In Neural Word Embeddings" by Andrew Trask, Phil Michalak and John Liu.
Implementation of DeepER system (record linkage with neural networks)
word embedding with word2vec, doc2vec algorithms on friends tv show corpus/dataset
Multi-Purpose support library developed during my PhD. It's always Work-In-Progress.
a non-neural network approach for word embedding
Generating TV scripts based on `Seinfeld` using recurrent neural networks
Turkish word2vec trained with Wikipedia dataset
This project aims at analysing with authomatic tools the new reality of 'entreprecariat', through a corpus of books related to the current labour market.
Ukraine Russia war tweet Analysis using Natural Language Processing NLP (Sentimental Analysis)
Using Machine Learning and Deep Learning Techniques [ Embedding ] | Neural Network [ LSTM ] in NLP for Twitter Sentiment Analysis.
Language Translation using Sequence to Sequence Recurrent Neural Network
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