wordembedding

A curated list of pretrained sentence and word embedding models

Compute Sentence Embeddings Fast!

Using pre trained word embeddings (Fasttext, Word2Vec)

Dict2vec is a framework to learn word embeddings using lexical dictionaries.

Code for "Effective Dimensionality Reduction for Word Embeddings".

Facebook fastText database in SQLite with Go API

Bi-Directional Attention Flow for Machine Comprehensions

Persian word embedding ( نشاننده واژه ها فارسی | تعبیه سازی کلمات فارسی )

👚 Speedy Word Embedding Association Test & Extras using R

股市新聞信息與過去股價作為預測股價特徵時應提取文章數量及方法

Data Mining Lab Session 2 (Fall 2017)

WebApplication for Similarity between Professor and Keyword based on WordEmbedding

My PhD thesis with all its source files, including all .tex files and images created, as well as the slides of my defense.

Wrapper of Gensim word2vec along with T-SNE visualization

Classification of "BBC News" and comparison of performance between 3 types of model's architectures. Then 2D word embedding visualization using PCA and 3D word embedding visualisation using T-SNE

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.

This a part of Kaggle Competion, Toxic Comment Classification Challenge by Jigsaw .This was a multilabel classification challenge.This code is a improved version of my submission in the competion.

A Restful Web service to provide wordembedding functions

D3-Network Visualization with WordEmbedding Space

A neural networks project that categorizes different poems in the dataset according to their genre

Content-based recommendation system for cosmetics

A Jupyter Notebook containing methods for common tasks related to the field of Natural Language Processing

A word embedding is a learned representation for text where words that have the same meaning have a similar representation.Word embeddings are in fact a class of techniques where individual words are represented as real-valued vectors in a predefined vector space. Each word is mapped to one vector and the vector values are learned in a way that resembles a neural network

Adaptive Skip-gram implementation in Julia

Natural Language processing with Disaster Tweets using word embeddings.

中文词向量