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 )

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

Wrapper of Gensim word2vec along with T-SNE visualization

Data Mining Lab Session 2 (Fall 2017)

WebApplication for Similarity between Professor and Keyword based on WordEmbedding

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

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

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

Content-based recommendation system for cosmetics

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

D3-Network Visualization with WordEmbedding Space

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

The aim of this project is to discover OOV(out of vocabulary) from Sina Weibo and to understand OOV by using the Word2Vec model. The first step was generated word lists through Mutual information and Left and Right Entropy Measures from news corpus of Sina Weibo was crawled, and OOV was extracted from the word lists through online dictionaries. The second step was extracted the relevant corpus containing OOV from Weibo. The third step, a third-party tool was used to divide the corpus into words and to obtain the distributed representation of words using Word2Vec's CBOW(continues bag of word) and Skip-Gram models. The fourth step was distributed representation information is used to compute words that are similar to the OOV in order to achieve semantic understanding of the OOV.The final result model has a high rate of correct word comprehension and is able to understand most of the OOV.

Polysemy Embedding - Iterative approach to address the sense based embedding

Adaptive Skip-gram implementation in Julia

'AI Hiphop lyrics Generator🎙' project which makes hiphop lyrics based on a few user's keywords liks 'love', 'money'.

中文词向量

Bengali Word Embedding by using Polygot2