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limdu - Machine-learning for Node.js
Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.
classifier classification categorization text-classification natural-lanaguage-understanding machine-learning multi-label multilabel multi-class multiclass online-learning naive-bayes winnow perceptron svm linear-svm binary-relevance one-vs-allfastText - Library for fast text representation and classification.
fastText is a library for efficient learning of word representations and sentence classification. You can find answers to frequently asked questions on our website.
text-classification text-representation classificationtext_classification - all kinds of text classificaiton models and more with deep learning
the purpose of this repository is to explore text classification methods in NLP with deep learning. sentence similarity project has been released you can check it if you like.
classification nlp fasttext textcnn textrnn tensorflow multi-label multi-class attention-mechanism text-classification convolutional-neural-networks sentence-classification memory-networksHanLP - 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-clusteringsnips-nlu - Snips Python library to extract meaning from text
Snips NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information. To find out how to use Snips NLU please refer to our documentation, it will provide you with a step-by-step guide on how to use and setup our library.
nlp nlu machine-learning data-science text-classification intent-classification ner named-entity-recognition slot-filling intent-parser information-extraction snips natural-language-processingNeuronBlocks - NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
NeuronBlocks is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages. NeuronBlocks consists of two major components: Block Zoo and Model Zoo.
question-answering deep-learning pytorch natural-language-processing text-classification artificial-intelligence dnn qna text-matching knowledge-distillation model-compressiontext-analytics-with-python - Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer
Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.
text-analytics text-summarization text-classification natural-language natural-language-processing clustering sentiment semantic sentiment-analysis nltk stanford-nlp spacy pattern scikit-learn gensimdelta - DELTA is a deep learning based natural language and speech processing platform.
DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper.
nlp deep-learning tensorflow speech sequence-to-sequence seq2seq speech-recognition text-classification speaker-verification nlu text-generation emotion-recognition tensorflow-serving tensorflow-lite inference asr serving front-endtf-rnn-attention - Tensorflow implementation of attention mechanism for text classification tasks.
Tensorflow implementation of attention mechanism for text classification tasks. Inspired by "Hierarchical Attention Networks for Document Classification", Zichao Yang et al. (http://www.aclweb.org/anthology/N16-1174).
attention tensorflow rnn text-classification sentiment-analysismeta - A Modern C++ Data Sciences Toolkit
Doxygen documentation can be found here. We have walkthroughs for a few different parts of MeTA on the MeTA homepage.
nlp nlp-parsing search-engine inverted-index pos-tag text-analysis text-analytics text-classification language-modeling graph-algorithms c-plus-plus word-embeddingscnn-text-classification-tf-chinese - CNN for Chinese Text Classification in Tensorflow
This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.
text-classification convolutional-neural-networks tensorflow cnn deep-learning chinese nlpzhihu-text-classification - [2017知乎看山杯 多标签 文本分类] ye组(第六名) 解题方案
和 creat_batch_data.py 相同，只是对 content 部分进行句子划分。用于分层模型。 划分句子长度： wd_title_len = 30, wd_sent_len = 30, wd_doc_len = 10.(即content划分为10个句子，每个句子长度为30个词) ch_title_len = 52, ch_sent_len = 52, ch_doc_len = 10. 不划分句子： wd_title_len = 30, wd_content_len = 150. ch_title_len = 52, ch_content_len = 300.
multi-label text-classification tensorflow lstm textcnn hantext-classification-models-tf - Tensorflow implementations of Text Classification Models.
Tensorflow implementation of Text Classification Models. Semi-supervised text classification(Transfer learning) models are implemented at [dongjun-Lee/transfer-learning-text-tf].
tensorflow text-classificationwhatlang-rs - Natural language detection library for Rust
Natural language detection for Rust with focus on simplicity and performance.For more details (e.g. how to blacklist some languages) please check the documentation.
language nlp text-analysis text-classificationpython-stop-words - Get list of common stop words in various languages in Python
Get list of common stop words in various languages in Python. Python-stop-words has been originally developed for Python 2, but has been ported and tested for Python 3.
text-classificationfeedIO - A Feed Aggregator that Knows What You Want to Read.
A Feed Aggregator that Knows What You Want to Read.
news text-classification aggregator feed-reader news-reader fake-newswatson-document-classifier - Augment IBM Watson Natural Language Understanding APIs with a configurable mechanism for text classification, uses IBM Data Science Experience (DSX)
Read this in other languages: 한국어. In this developer journey we will use Jupyter notebooks in IBM Data Science experience(DSX) to augment IBM Watson Natural Language Understanding API output through configurable mechanism for text classification.
natural-language text-classification watson-natural-language ibm-developer-technology-cognitive ibmcode nlu watson natural-language-understanding dsx data-science-experience nlp natural-language-processingdeep-learning - Deep Learning Bootcamp
The objective for the Deep Learning bootcamp is to ensure that the participants have enough theory and practical concepts of building a deep learning solution in the space of computer vision and natural language processing. Post the bootcamp, all the participants would be familiar with the following key concepts and would be able to apply them to a problem. These are reference materials which have excellent explanations - visual, interactive, math or code driven in text, video, app or notebook format - about Machine Learning and Deep Learning. We have found them useful in our own learning journey. We hope they will help you in yours.
deep-learning deep-neural-networks image-classification text-classification keras tensorflowtext-mining - Text Mining in Python
Text Mining in Python
text-mining text-classification text-processingomnicat-bayes - Naive Bayes text classification implementation as an OmniCat classifier strategy
A Naive Bayes text classification implementation as an OmniCat classifier strategy. See rdoc for detailed usage.
naive-bayes-classifier text-classification sentiment-analysis tokenizer stopwords
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