skipgram

Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP

Skipgram Negative Sampling implemented in PyTorch

A word2vec negative sampling implementation with correct CBOW update.

结合python一起学习自然语言处理 (nlp): 语言模型、HMM、PCFG、Word2vec、完形填空式阅读理解任务、朴素贝叶斯分类器、TFIDF、PCA、SVD

Extremely simple and fast word2vec implementation with Negative Sampling + Sub-sampling

This repository contains the "tensorflow" implementation of our paper "graph2vec: Learning distributed representations of graphs".

Colibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way. At the core is the tool ``colibri-patternmodeller`` whi ch allows you to build, view, manipulate and query pattern models.

Context-sensitive word embeddings with subwords. In Rust.

TensorFlow implementation of word2vec applied on https://www.kaggle.com/tamber/steam-video-games dataset, using both CBOW and Skip-gram.

Explaining textual analysis tools in Python. Including Preprocessing, Skip Gram (word2vec), and Topic Modelling.

PyTorch implementation of the Word2Vec (Skip-Gram Model) and visualizing the trained embeddings using TSNE

Finds out symptoms similar to a given symptom, from a symptom-disease data set.

A word2vec port for Windows.

This repository contains the TensorFlow implemtation of subgraph2vec (KDD MLG 2016) paper

Efficient learning of word representations

Billion-scale node2vec in scala-spark

word2vec implementation (for skip-gram and cbow) and simple application of word2vec in sentiment analysis

A neural network-based AI chatbot has been designed that uses LSTM as its training model for both encoding and decoding. The chatbot works like an open domain chatbot that can answer day-to-day questions involved in human conversations. Words embeddings are the most important part of designing a neural network-based chatbot. Glove Word Embedding and Skip-Gram models have been used for this task.

This repo contains my solution to the Stanford course "NLP with Deep Learning" under CS224n code. Here, you can find the solution for all classes starting form 2018

Bengali Word Embedding