Topic Modelling for Humans

Machine learning, in numpy

大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP

Extract Keywords from sentence or Replace keywords in sentences.

Python interface to Google word2vec

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

Pre-trained word vectors of 30+ languages

A curated list of awesome embedding models tutorials, projects and communities.

Beautiful visualizations of how language differs among document types.

A fast, efficient universal vector embedding utility package.

兜哥出品 <一本开源的NLP入门书籍>

Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. All codes are implemented intensorflow 2.0.

🦆 Contextually-keyed word vectors

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

基于Pytorch和torchtext的自然语言处理深度学习框架，包含序列标注、文本分类、句子关系、文本生成、结构分析、五大功能模块，已实现了命名实体识别、中文分词、词性标注、语义角色标注、情感分析、关系抽取、语言模型、文本相似度、文本蕴含、依存句法分析、词向量训练、聊天机器人、机器翻译、文本摘要等功能。框架功能丰富，开箱可用，极易上手！基本都是学习他人实现然后自己修改融合到框架中，没有细致调参，且有不少Bug～

Fast vectorization, topic modeling, distances and GloVe word embeddings in R.

CS224n: Natural Language Processing with Deep Learning Assignments Winter, 2017

Four word embedding models implemented in Python. Supporting arbitrary context features

Starter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more.

Deep neural network framework for multi-label text classification

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

Document-oriented search engine with HTTP API.

中文詞向量訓練教學

Fast word vectors with little memory usage in Python

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

Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"

Programming Assignments and Lectures for Stanford's CS 224: Natural Language Processing with Deep Learning

Word Embeddings (e.g. Word2Vec) in Go!

word2vec, sentence2vec, machine reading comprehension, dialog system, text classification, pretrained language model (i.e., XLNet, BERT, ELMo, GPT), sequence labeling, information retrieval, information extraction (i.e., entity, relation and event extraction), knowledge graph, text generation, network embedding

Multiple implementations for abstractive text summurization , using google colab