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gensim - Topic Modelling for Humans
Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia.
gensim topic-modeling information-retrieval machine-learning natural-language-processing nlp data-science data-mining word2vec word-embeddings text-summarization neural-network document-similarity word-similarity fasttextflashtext - Extract Keywords from sentence or Replace keywords in sentences.
This module can be used to replace keywords in sentences or extract keywords from sentences. It is based on the FlashText algorithm. Documentation can be found at FlashText Read the Docs.
search-in-text keyword-extraction nlp word2vec data-extractionsense2vec - 🦆 Use NLP to go beyond vanilla word2vec
sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting, detailed and context-sensitive word vectors. For an interactive example of the technology, see our sense2vec demo that lets you explore semantic similarities across all Reddit comments of 2015. This library is a simple Python/Cython implementation for loading and querying sense2vec models. While it's best used in combination with spaCy, the sense2vec library itself is very lightweight and can also be used as a standalone module. See below for usage details.
spacy nlp natural-language-processing word2vec sense2vec gensim gensim-word2vec machine-learningHanLP - 自然语言处理 中文分词 词性标注 命名实体识别 依存句法分析 关键词提取 新词发现 短语提取 自动摘要 文本分类 拼音简繁
汉语言处理包
nlp natural-language-processing hanlp crf hmm trie textrank doublearraytrie neural-network chinese-word-segmentation text-mining pos-tagging dependency-parser text-classification word2vec perceptron named-entity-recognition text-clusteringwordvectors - Pre-trained word vectors of 30+ languages
This project has two purposes. First of all, I'd like to share some of my experience in nlp tasks such as segmentation or word vectors. The other, which is more important, is that probably some people are searching for pre-trained word vector models for non-English languages. Alas! English has gained much more attention than any other languages has done. Check this to see how easily you can get a variety of pre-trained English word vectors without efforts. I think it's time to turn our eyes to a multi language version of this. Nearing the end of the work, I happened to know that there is already a similar job named polyglot. I strongly encourage you to check this great project. How embarrassing! Nevertheless, I decided to open this project. You will know that my job has its own flavor, after all.
vector word2vec language fasttextword2vec - Python interface to Google word2vec
Python interface to Google word2vec. Training is done using the original C code, other functionality is pure Python with numpy.
word2vecTensorFlow-Tutorials - 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다
텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다.
neural-network tensorflow mnist autoencoder rnn deep-learning tutorial chatbot seq2seq dqn word2vec cnn gan inceptiontext2vec - Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP). To learn how to use this package, see text2vec.org and the package vignettes. See also the text2vec articles on my blog.
word2vec text-mining natural-language-processing glove vectorization topic-modeling word-embeddings latent-dirichlet-allocationRepo-2017 - Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano
Welcome to my GitHub repo. I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I developed.
anomaly-detection deep-learning autoencoder keras keras-models denoising-autoencoders generative-adversarial-network glove keras-layer word2vec nlp natural-language-processing sentiment-analysis opencv segnet resnet-50 variational-autoencoder t-sne svm-classifier latent-dirichlet-allocationmagnitude - A fast, efficient universal vector embedding utility package.
A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim, but can be used as a generic key-vector store for domains outside NLP. Vector space embedding models have become increasingly common in machine learning and traditionally have been popular for natural language processing applications. A fast, lightweight tool to consume these large vector space embedding models efficiently is lacking.
natural-language-processing nlp machine-learning vectors embeddings word2vec fasttext glove gensim fast memory-efficient machine-learning-library word-embeddingsCS224n - CS224n: Natural Language Processing with Deep Learning Assignments Winter, 2017
CS224n: Natural Language Processing with Deep Learning Assignments Winter, 2017
cs224n deep-learning natural-language-processing word2vec rnn tensorflowpycadl - Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
This package is part of the Kadenze Academy program Creative Applications of Deep Learning w/ TensorFlow. from cadl import and then pressing tab to see the list of available modules.
deep-learning neural-network tutorial mooc gan vae vae-gan pixelcnn wavenet magenta nsynth tensorflow celeba cyclegan dcgan word2vec glove autoregressive conditional coursepractical-1 - Oxford Deep NLP 2017 course - Practical 1: word2vec
For this practical, you'll be provided with a partially-complete IPython notebook, an interactive web-based Python computing environment that allows us to mix text, code, and interactive plots. We will be training word2vec models on TED Talk and Wikipedia data, using the word2vec implementation included in the Python package gensim. After training the models, we will analyze and visualize the learned embeddings.
word2vec nlp natural-language-processing deep-learning oxfordgo2vec - Read and use word2vec vectors in Go
This is a package for reading word2vec vectors in Go and finding similar words and analogies.
word2vec embeddingsNWord2Vec - :speech_balloon: C# library for working with Word2Vec models
C# library for working with Word2Vec models. First build your model with the word2vec command line tools.
word2vecpujangga - Pujangga - Indonesian Natural Language Processing REST API, an Interface for InaNLP and Deeplearning4j's Word2Vec
An interface for InaNLP and Deeplearning4j's Word2Vec for Indonesian (Bahasa Indonesia) in the form of REST API.
natural-language-processing deep-learning word2vec deeplearning4j play-framework bahasa-indonesia indonesian-languagewikimark - get a sens of it
wikimark goal is to give you an idea of what the text is about. You can also use your own corpus.
machine-learning natural-language-processing structured-data gensim word2vec doc2vec scikit-learn similaritychainer-notebooks - Jupyter notebooks for Chainer hands-on
Jupyter notebooks for Chainer hands-on
chainer deep-learning jupyter-notebook dqn word2vec rnn language-model cnn convolutional-neural-networksexperiments - Some research experiments
Some research experiments I have done during the years. Most of the notes can be found on City of Wings.
deep-learning scrapy jupyter-notebook sacred word2veclanguagecrunch - LanguageCrunch NLP server docker image
sentence: The new twitter is so weird. Seriously. Why is there a new twitter? What was wrong with the old one? Fix it now.
nlp spacy-nlp wordnet coreference-resolution natural-language-processing sentiment-analysis relation-extraction word2vec
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