├── interview.md ├── other_repo.md ├── blog.md ├── awesome_list.md └── README.md /interview.md: -------------------------------------------------------------------------------- 1 | # 面试求职资源集锦 2 | - [GitHub-Chinese-Top-Charts](https://github.com/kon9chunkit/GitHub-Chinese-Top-Charts#All-Language) - GitHub中文排行榜，帮助你发现高分优秀中文项目、更高效地吸收国人的优秀经验成果 3 | - [2019 Autumn Recruitment Experience](https://github.com/zslomo/2019-Autumn-recruitment-experience) 4 | - [Algorithm Interview Notes Chinese](https://github.com/DarLiner/Algorithm_Interview_Notes-Chinese) 5 | - [CS-Notes](https://github.com/CyC2018/CS-Notes) - 技术面试必备基础知识 6 | - [cv-interview](https://github.com/donnyyou/cv-interview) - CV岗常见面试题 7 | - [Deep Learning 500 Questions](https://github.com/scutan90/DeepLearning-500-questions) - 深度学习500问 8 | - [Deep Learning Book QA](https://github.com/elviswf/DeepLearningBookQA_cn) - 深度学习花书QA 9 | - [DL Interview](https://github.com/ShanghaiTechAIClub/DLInterview) - 深度学习面试题收集 10 | - [Interview-code-practice-python](https://github.com/leeguandong/Interview-code-practice-python) 11 | - [CodingInterviewChinese2](https://github.com/zhedahht/CodingInterviewChinese2) - 剑指Offer第2版源代码 12 | - [Jianzhi Offer C++ Implementation](https://github.com/gatieme/CodingInterviews) 13 | - [Jianzhi Offer Python Implementation](https://github.com/JushuangQiao/Python-Offer) 14 | - [km1994 / NLP-Interview-Notes](https://github.com/km1994/NLP-Interview-Notes) - 本项目是作者们根据个人面试和经验总结出的自然语言处理(NLP)面试准备的学习笔记与资料 15 | - [Python & JAVA Solutions for Leetcode](https://github.com/qiyuangong/leetcode) 16 | - [leetcode题解](https://github.com/azl397985856/leetcode) - 推荐 17 | - [Making-It/Code](https://github.com/Making-It/Code) 18 | - [wolverinn/Waking-Up](https://github.com/wolverinn/Waking-Up) - 计算机基础（计算机网络/操作系统/数据库/Git...）面试问题全面总结 19 | - [Jack-Cherish/LeetCode](https://github.com/Jack-Cherish/LeetCode) 20 | - [YaxeZhang/Just-Code](https://github.com/YaxeZhang/Just-Code) 21 | - [Top 10国际大厂人工智能岗位经典面试题精选](https://mp.weixin.qq.com/s/FUpPIZP0hzUWNXZobjGYPw) 22 | - [reverse-interview-zh](https://github.com/yifeikong/reverse-interview-zh) - 技术面试最后反问面试官的话 23 | - [ML-NLP](https://github.com/NLP-LOVE/ML-NLP) - 机器学习、NLP面试中常考到的知识点和代码实现 24 | - [NLPer-Interview](https://github.com/songyingxin/NLPer-Interview) - 老宋的茶书会整理的NLP算法工程师相关的面试题 25 | - [interview_internal_reference](https://github.com/0voice/interview_internal_reference) - 2019年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总 26 | - [AI Job Recommend](https://github.com/amusi/AI-Job-Recommend) 27 | - [Awesome Resume for Chinese](https://github.com/dyweb/awesome-resume-for-chinese) - 适合中文的简历模板 28 | - [labuladong/fucking-algorithm](https://github.com/labuladong/fucking-algorithm) 29 | - [NLP_ability](https://github.com/DA-southampton/NLP_ability) 30 | - [sladesha / Reflection_Summary](https://github.com/sladesha/Reflection_Summary) - 算法理论基础知识应知应会 31 | -------------------------------------------------------------------------------- /other_repo.md: -------------------------------------------------------------------------------- 1 | ### Universal 2 | - [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) - A curated list of awesome Machine Learning frameworks, libraries and software 3 | - [dimensionality_reduction_alo_codes](https://github.com/heucoder/dimensionality_reduction_alo_codes) 4 | 5 | ### Reinforcement Learning 6 | - [NeuronDance / DeepRL](https://github.com/NeuronDance/DeepRL) 7 | - [RchalYang / TorchRL](https://github.com/RchalYang/torchrl) - Pytorch implementation of reinforcement learning algorithms 8 | - [Yonv1943 / DL_RL_Zoo](https://github.com/Yonv1943/DL_RL_Zoo) - Lightweight, stable, efficient PyTorch implement of reinforcement learning 9 | 10 | ### AutoML 11 | - [Microsoft / NNI](https://github.com/microsoft/nni) 12 | - [Huawei / Vega](https://github.com/huawei-noah/vega) - AutoML tools chain 13 | 14 | ### Adversarial Learning 15 | - [9310gaurav / virtual-adversarial-training](https://github.com/9310gaurav/virtual-adversarial-training) - Pytorch implementation of Virtual Adversarial Training 16 | - [DSE-MSU / DeepRobust](https://github.com/DSE-MSU/DeepRobust) - A pytorch adversarial library for attack and defense methods on images and graphs 17 | - [lyakaap / VAT-pytorch](https://github.com/lyakaap/VAT-pytorch) - Virtual Adversarial Training (VAT) implementation for PyTorch 18 | - [THUNLP / OpenAttack](https://github.com/thunlp/OpenAttack]) - About An Open-Source Package for Textual Adversarial Attack. 19 | 20 | ### Transfer Learning 21 | - [THUML / Transfer-Learning-Library](https://github.com/thuml/Transfer-Learning-Library) 22 | 23 | ### Computer Vision 24 | - [vdumoulin / convolution arithmetic](https://github.com/vdumoulin/conv_arithmetic) - A technical report on convolution arithmetic in the context of deep learning 25 | 26 | ### Graph Neural Network 27 | - [facebookresearch / PyTorch-BigGraph](https://github.com/facebookresearch/PyTorch-BigGraph) - Generate embeddings from large-scale graph-structured data 28 | - [rusty1s / pytorch_geometric](https://github.com/rusty1s/pytorch_geometric) - Geometric Deep Learning Extension Library for PyTorch 29 | - [zhouchunpong / GCN_Keras](https://github.com/zhouchunpong/GCN_Keras) - Graph Convolutional Network with Keras 30 | 31 | ### Serving 32 | - [PyTorch / serve](https://github.com/pytorch/serve) - Model Serving on PyTorch 33 | - [Tensorflow / serving](https://github.com/tensorflow/serving) - A flexible, high-performance serving system for machine learning models 34 | - [Microsoft / MMdnn](https://github.com/microsoft/MMdnn) - A set of tools to help users inter-operate among different deep learning frameworks 35 | 36 | ### Tools 37 | - [cgnorthcutt / cleanlab](https://github.com/cgnorthcutt/cleanlab) - Find label errors in datasets, weak supervision, and learning with noisy labels 38 | - [Kaleidophon / token2index](https://github.com/Kaleidophon/token2index) - A lightweight but powerful library to build token indices for NLP tasks 39 | - [NVIDIA-AI-IOT/torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt) - An easy to use PyTorch to TensorRT converter 40 | - [vaaaaanquish / cloudia](https://github.com/vaaaaanquish/cloudia) - Tools to easily create a word cloud 41 | 42 | ### Others 43 | - [Note-by-LaTeX](https://github.com/wklchris/Note-by-LaTeX) 44 | -------------------------------------------------------------------------------- /blog.md: -------------------------------------------------------------------------------- 1 | # 优秀博客与文章 2 | 个人收集的深度学习、自然语言处理与推荐系统领域优秀博客与文章，基于博客字母和内容关联度排序。如果某个大牛博客有多篇优秀文章，则直接推荐其博客主页，不再列举其具体文章，由读者自己去选择。 3 | 4 | ## Blog 5 | - [**52nlp**](http://www.52nlp.cn) - 52nlp的大佬是我的NLP入门的领路人，知识图谱、NLPJob等站点也是他建立的 6 | - [**BAMTERCELBOO**](https://bamtercelboo.github.io) - 博客内容在词向量等任务上有很深的认识 [[知乎]](https://www.zhihu.com/people/bamtercelboo/posts) 7 | - [**CreateMoMo**](https://createmomo.github.io) - BiLSTM-CRF的几篇教程很不错 8 | - [**Google AI Blog**](https://ai.googleblog.com/) 9 | - [**OpenAI Blog**](https://openai.com/blog/) 10 | - [**阿里中间件团队**](https://blog.51cto.com/aliapp) 11 | - [**安迪的写作间**](https://www.zhihu.com/people/andy_yangz/posts) 12 | - [**高开远**](https://blog.csdn.net/Kaiyuan_sjtu/article/details/89788314) 分享了许多高质量的ABSA、NER等任务的论文见解 13 | - [**寒小阳**](https://blog.csdn.net/han_xiaoyang/article/list/1) 在推荐系统和NLP等方面有许多深入浅出的博客 14 | - [**科学空间**](https://spaces.ac.cn) - 苏神苏剑林的博客 15 | - [**老宋的茶书会**](https://www.zhihu.com/people/songyingxin/posts) - 分享了许多优秀的BERT等方面的论文见解 16 | - [**李理的博客**](http://fancyerii.github.io) - 作者还发布了一本不错的中文书籍[《深度学习理论与实战：提高篇》](http://fancyerii.github.io/2019/03/14/dl-book/) 17 | - [**李如**](https://www.zhihu.com/people/rumor-lee/posts) 18 | - [**廖雪峰**](https://www.liaoxuefeng.com/wiki/1177760294764384) - 廖雪峰大牛的各类教程质量不用赘述 19 | - [**刘知远**](https://www.zhihu.com/people/zibuyu9/posts) - 刘老师分享的许多科研指导和THUNLP团队的开源项目都非常好 20 | - [**码农场hankcs**](http://www.hankcs.com) - hanlp开发者 21 | - [**美团点评技术团队**](https://tech.meituan.com/) 22 | - [**深度传送门**](https://www.zhihu.com/people/yixiaonongfu/posts) - 深度推荐系统与CTR预估，微博同名 23 | - [**石晓文**](https://www.jianshu.com/nb/21403842) - 深度学习遇上推荐系统系列博客非常不错 24 | - [**王喆的机器学习笔记**](https://zhuanlan.zhihu.com/wangzhenotes) - 推荐系统、计算广告等机器学习领域前沿知识 25 | - [**徐啸**](https://www.zhihu.com/people/xuxiao-looper/posts) 26 | - [**杨旭东**](https://www.zhihu.com/people/yang-xu-dong-6/posts) - 阿里推荐系统工程师 27 | - [**忆臻**](https://www.zhihu.com/people/qinlibo_nlp/posts) 28 | - [**张俊林**](https://www.zhihu.com/people/zhang-jun-lin-76/activities) - 张俊林老师几乎所有文章都可谓深入浅出字字珠玑见解独到（不是我吹） 29 | - [**知乎技术专栏**](https://zhuanlan.zhihu.com/hackers) - 知乎官方的技术内容分享，其中算法方面有孙付伟等大牛的分享，如 30 | - [Graph Embedding 及其在知乎的实践](https://zhuanlan.zhihu.com/p/82962081) 31 | 32 | ## Article 33 | - [PaperWeekly / NLP中各框架对变长序列的处理全解](https://mp.weixin.qq.com/s/KxgA1U-mh9Tc1J1T9Aedrw) 34 | - [PyTorch实战指南](https://zhuanlan.zhihu.com/p/29024978) 35 | - [PyTorch中如何处理RNN输入变长序列padding](https://zhuanlan.zhihu.com/p/34418001) 36 | - [丁香园 / 医疗搜索中的query词权重算法探索](https://mp.weixin.qq.com/s/JCdzhd1wBKIzDkoqW87OAg) 37 | - [夕小瑶的卖萌屋 / 万字长文梳理文本生成评价指标](https://zhuanlan.zhihu.com/p/144182853) 38 | - [教你几招搞定 LSTMs 的独门绝技（附代码）](https://zhuanlan.zhihu.com/p/40391002) 39 | - [TensorFlow Wide And Deep模型详解与应用](https://blog.csdn.net/heyc861221/article/details/80131369) 40 | - [初入NLP领域的一些小建议](https://zhuanlan.zhihu.com/p/59184256) 41 | - [互联网时代的社会语言学：基于SNS的文本数据挖掘](http://www.matrix67.com/blog/archives/5044) 42 | - [基于凝聚度和自由度的非监督词库生成](http://zhanghonglun.cn/blog/project/基于凝聚度和自由度的非监督词库生成/) 43 | - [基于今日头条开源数据的词共现、新热词发现、短语发现](https://blog.csdn.net/sinat_26917383/article/details/80454736) 44 | - [限定域文本语料的短语挖掘（Phrase Mining）](https://mp.weixin.qq.com/s?__biz=MzIwNzc2NTk0NQ==&mid=2247485406&idx=2&sn=9a0b7f06184fb2da452620e429eca748&chksm=970c2f08a07ba61ec51fb36d5c2aabd8ba3c27b73eeebfd62118810ddf03acc6b541dccec9da&mpshare=1&scene=1&srcid=&sharer_sharetime=1578910444068&sharer_shareid=0958587d4e115417a79743c81caec1c8&key=7627459f896ffec6ae7c11ed405906976de16aa7f417bd3256d64fa3adaffd711eeb1ff1a670e66301822cde5f5148b337f2c33cf3e1d296ecb6fcfee7603324a894a41c2a04b366403d7314817c2719&ascene=1&uin=Mjg1NTM0NDcyMw%3D%3D&devicetype=Windows+10&version=62080079&lang=zh_CN&exportkey=Ax7uvo1V12Jq6iLFFrMFelY%3D&pass_ticket=i8bk%2FT42OP%2F5AUId%2BQKi0Krdjm1I8rT3mWpp8xiOHSjbiYV5vSI%2F48loWKOZ29Yq) 45 | - [史上最可爱的关系抽取指南？从一条规则到十个开源项目](https://mp.weixin.qq.com/s/PGjIWuBGAVG9C9X9d1aKaQ) 46 | - [详解深度学习中的Normalization，BN/LN/WN](https://zhuanlan.zhihu.com/p/33173246) 47 | - [中文分词十年又回顾2007-2017--简报](https://zhuanlan.zhihu.com/p/56107108) 48 | - [正态分布的前世今生](https://github.com/panyang/AINLP-Resource/tree/master/rickjin) 49 | - [一文读懂深度学习中的N种卷积](https://mp.weixin.qq.com/s/6300oADp7QN5SDPrv2yAtQ) 50 | - [【杂谈】那些酷炫的深度学习网络图怎么画出来的？](https://zhuanlan.zhihu.com/p/60146525) 51 | -------------------------------------------------------------------------------- /awesome_list.md: -------------------------------------------------------------------------------- 1 | - [AntixK / PyTorch-VAE](https://github.com/AntixK/PyTorch-VAE) - A Collection of Variational Autoencoders (VAE) in PyTorch 2 | - [bisheng / Awesome-QG](https://github.com/bisheng/Awesome-QG) - Question Generation Papers Collection 3 | - [teacherpeterpan / Question-Generation-Paper-List](https://github.com/teacherpeterpan/Question-Generation-Paper-List) - A summary of must-read papers for Neural Question Generation 4 | - [snakeztc / NeuralDialogPapers](https://github.com/snakeztc/NeuralDialogPapers) 5 | - [sz128 / NLU_datasets_with_task_oriented_dialogue](https://github.com/sz128/NLU_datasets_with_task_oriented_dialogue) - datasets of natural language understanding and dialogue state tracking 6 | - [yizhen20133868 / Awesome-SLU-Survey](https://github.com/yizhen20133868/Awesome-SLU-Survey) - Tracking the progress in SLU (resources, code, and new frontiers etc.) 7 | - [ChenChengKuan / awesome-text-generation](https://github.com/ChenChengKuan/awesome-text-generation) 8 | - [chingyaoc / pytorch-REINFORCE](https://github.com/chingyaoc/pytorch-REINFORCE) - PyTorch Implementation of REINFORCE for both discrete & continuous control 9 | - [The Big Bad NLP Database](https://datasets.quantumstat.com/) 10 | - [THUNLP / Graph Neural Network Papers](https://github.com/thunlp/GNNPapers) 11 | - [THUNLP / Knowledge Representation Learning Papers](https://github.com/thunlp/KRLPapers) 12 | - [THUNLP / Legal Intelligence Papers](https://github.com/thunlp/LegalPapers) 13 | - [THUNLP / Machine Reading Comprehension Papers](https://github.com/thunlp/RCPapers) 14 | - [THUNLP / Network Representation Learning Papers](https://github.com/thunlp/NRLPapers) 15 | - [THUNLP / Machine Translation Papers](https://github.com/THUNLP-MT/MT-Reading-List) 16 | - [THUNLP / Neural Information Retrieval Papers](https://github.com/thunlp/NeuIRPapers) 17 | - [THUNLP / Neural Relation Extraction Papers](https://github.com/thunlp/NREPapers) 18 | - [THUNLP / Poetry Generation Papers](https://github.com/THUNLP-AIPoet/PaperList) 19 | - [THUNLP / Pretrained Language Model Papers](https://github.com/thunlp/PLMpapers) 20 | - [THUNLP / Sememe Computation Papers](https://github.com/thunlp/SCPapers) 21 | - [THUNLP / Text Generation Papers](https://github.com/THUNLP-MT/TG-Reading-List) 22 | - [THUNLP / Textual Adversarial Attack and Defense Papers](https://github.com/thunlp/TAADpapers) 23 | - [NiuTrans / ABigSurvey](https://github.com/NiuTrans/ABigSurvey) - A collection of 400+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML) 24 | - [awesome graph neural network](https://github.com/nnzhan/Awesome-Graph-Neural-Networks) 25 | - [awesome graph classification](https://github.com/benedekrozemberczki/awesome-graph-classification) 26 | - [Research-Line](https://github.com/ConanCui/Research-Line) - 他人的知识图谱、异构网络、图嵌入与推荐系统论文集 27 | - [awesome knowledge graph (shaoxiongji)](https://github.com/shaoxiongji/awesome-knowledge-graph) 28 | - [awesome knowledge graph (husthuke)](https://github.com/husthuke/awesome-knowledge-graph) 29 | - [joeat1 / GNN_note](https://github.com/joeat1/GNN_note) - 图神经网络整理 30 | - [NREPapers2019](https://github.com/WindChimeRan/NREPapers2019) 31 | - [awesome relation extraction](https://github.com/roomylee/awesome-relation-extraction) 32 | - [EventExtractionPapers](https://github.com/BaptisteBlouin/EventExtractionPapers) 33 | - [awesome sentiment analysis](https://github.com/laugustyniak/awesome-sentiment-analysis#papers) 34 | - [awesome data augmentation](https://github.com/CrazyVertigo/awesome-data-augmentation) 35 | - [data augmentation review](https://github.com/AgaMiko/data-augmentation-review) 36 | - [nlp data augmentation](https://github.com/quincyliang/nlp-data-augmentation) 37 | - [awesome law NLP research work](https://github.com/bamtercelboo/Awesome-Law-NLP-Research-Work) - 法律NLP工作资源集 38 | - [awesome Chinese NLP](https://github.com/crownpku/Awesome-Chinese-NLP) - 中文自然语言处理资源集 39 | - [DL-NLP-Readings](https//github.com/IsaacChanghau/DL-NLP-Readings) - 他人的自然语言处理论文集 40 | - [Deep Learning with Electronic Health Record (EHR) Systems](https://practicalai.me/blog/deep-learning-with-ehr-systems) 41 | - [awesome deepfake materials](https://github.com/datamllab/awesome-deepfakes-materials) 42 | - [awesome kaldi](https://github.com/YoavRamon/awesome-kaldi) 43 | - [awesome speech](https://github.com/mxer/awesome-speech) 44 | - [BaeSeulki / NL2LF](https://github.com/BaeSeulki/NL2LF) - The Resources for "Natural Language to Logical Form 45 | - [yechens / NL2SQL](https://github.com/yechens/NL2SQL) - Text2SQL语义解析数据集、解决方案、paper资源整合项目 46 | - [wangdongdut / PaperWriting](https://github.com/wangdongdut/PaperWriting) - Paper Writing General Guidelines 47 | - [he-y / Awesome-Pruning](https://github.com/he-y/Awesome-Pruning) - A curated list of neural network pruning resources. 48 | - [BDBC-KG-NLP / QA-Survey](https://github.com/BDBC-KG-NLP/QA-Survey) - 北航大数据高精尖中心研究张日崇团队对问答系统的调研。包括知识图谱问答系统（KBQA）和文本问答系统（TextQA），每类系统分别对学术界和工业界进行调研。 49 | - [zhijing-jin / Text_Style_Transfer_Survey](https://github.com/zhijing-jin/Text_Style_Transfer_Survey) - This repo collects the articles for text attribute transfer 50 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 自然语言处理相关资源合集 2 | 本仓库用于存放我个人所需要的和收集的自然语言处理以及部分其他领域的资源合集。 3 | 4 | ## 书籍、课程与笔记 5 | - [CMU / CS 11-747 Spring 2019](http://phontron.com/class/nn4nlp2019/index.html) 6 | - [d2l-ai/d2l-zh](https://github.com/d2l-ai/d2l-zh) - 《动手学深度学习》：面向中文读者、能运行、可讨论。 7 | - [dsgiitr/d2l-pytorch](https://github.com/dsgiitr/d2l-pytorch) - This project reproduces the book Dive Into Deep Learning 8 | - [Deep Learning Book](https://www.deeplearningbook.org) 9 | - [Deep Learning Book中文版](https://github.com/exacity/deeplearningbook-chinese) 10 | - [Doraemonzzz / Learning-from-data](https://github.com/Doraemonzzz/Learning-from-data) - 记录Learning from data一书中的习题解答 11 | - [eastlakeside / Python进阶中文版](https://github.com/eastlakeside/interpy-zh) 12 | - [fengdu78 / lihang-code](https://github.com/fengdu78/lihang-code) - 《统计学习方法》的代码实现 13 | - [fly51fly / Practical_Python_Programming](https://github.com/fly51fly/Practical_Python_Programming) - 北邮《Python编程与实践》课程资料 14 | - [fly51fly / Principle-of-Web-Search](https://github.com/fly51fly/Principle-of-Web-Search) - 北京邮电大学“网络搜索原理”课程资料(2019) 15 | - [huzecong / oi-slides](https://github.com/huzecong/oi-slides) - 信息学竞赛讲课课件 16 | - [librauee / Reptile](https://github.com/librauee/Reptile) - Python3网络爬虫实战 17 | - [NiuTrans / MTBook](https://github.com/NiuTrans/MTBook) - 机器翻译：统计建模与深度学习方法 18 | - [NiuTrans / CNSurvey](https://github.com/NiuTrans/CNSurvey) - 一份中文综述文章列表（自然语言处理&机器学习） 19 | - [npubird / KnowledgeGraphCourse](https://github.com/npubird/KnowledgeGraphCourse) - 东南大学《知识图谱》研究生课程 20 | - [Xipeng Qiu / 神经网络与深度学习](https://nndl.github.io) 21 | - [ShusenTang / Dive-into-DL-PyTorch](https://github.com/ShusenTang/Dive-into-DL-PyTorch) - 动手学深度学习 22 | - [Stanford / CS224n Spring 2019](http://web.stanford.edu/class/cs224n/) 23 | - [Stanford / Speech and Language Processing 3rd](https://web.stanford.edu/~jurafsky/slp3/) 24 | - [TingsongYu / PyTorch_Tutorial](https://github.com/TingsongYu/PyTorch_Tutorial) - PyTorch模型训练实用教程 25 | - [Zhiyuan Liu / research_tao](https://github.com/zibuyu/research_tao) - NLP研究入门之道 26 | - [Zhiyuan Liu / Representation Learning for Natural Language Processing](https://rd.springer.com/book/10.1007%2F978-981-15-5573-2) 27 | - [zxdefying / pytorch_tricks](https://github.com/zxdefying/pytorch_tricks) - some tircks for PyTorch 28 | 29 | ## 代码、工具与项目 30 | ### 入门教程 31 | - [gaoisbest / NLP-Projects](https://github.com/gaoisbest/NLP-Projects) 32 | - [jvns / Pandas cookbook](https://github.com/jvns/pandas-cookbook) - Recipes for using Python's pandas library 33 | - [leerumor / nlp_tutorial](https://github.com/leerumor/nlp_tutorial) - NLP超强入门指南，包括各任务sota模型汇总（文本分类、文本匹配、序列标注、文本生成、语言模型），以及代码、技巧 34 | - [lyeoni / nlp-tutorial](https://github.com/lyeoni/nlp-tutorial) - A list of NLP tutorials 35 | - [yunjey / PyTorch Tutorial](https://github.com/yunjey/pytorch-tutorial) - PyTorch Tutorial for Deep Learning Researchers 36 | 37 | ### 通用深度学习自然语言处理框架与工具 38 | - [AllenAI / AllenNLP](https://github.com/allenai/allennlp) - An open-source NLP research library, built on PyTorch. 39 | - [BrikerMan / Kashgari](https://github.com/BrikerMan/Kashgari) - Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification 40 | - [dmlc / gluon-nlp](https://github.com/dmlc/gluon-nlp) - NLP made easy 41 | - [FacebookResearch / PyText](https://github.com/facebookresearch/pytext) - A natural language modeling framework based on PyTorch 42 | - [fastNLP / fastNLP](https://github.com/fastnlp/fastNLP) - A Modularized and Extensible NLP Framework. Currently still in incubation. 43 | - [flairNLP / flair](https://github.com/flairNLP/flair) - A very simple framework for state-of-the-art Natural Language Processing 44 | - [huggingface / transformers](https://github.com/huggingface/transformers) - 🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0. 45 | - [kolloldas / TorchNLP](https://github.com/kolloldas/torchnlp) - Easy to use NLP library built on PyTorch and TorchText 46 | - [ownthink / Jiagu](https://github.com/ownthink/Jiagu) - Jiagu深度学习自然语言处理工具 47 | - [QData / TextAttack](https://github.com/QData/TextAttack) - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP 48 | - [RUCAIBox / TextBox](https://github.com/RUCAIBox/TextBox) - An open-source library for building text generation system 49 | - [SpeechBrain / speechbrain](https://github.com/speechbrain/speechbrain) - A PyTorch-based Speech Toolkit 50 | - [StanfordNLP / stanza](https://github.com/stanfordnlp/stanza/) - Official Stanford NLP Python Library for Many Human Languages 51 | - [TensorFlow / tensor2tensor](https://github.com/tensorflow/tensor2tensor) - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. 52 | - [Tensorflow / models](https://github.com/tensorflow/models) - Models and examples built with TensorFlow 53 | - [THUNLP / OpenAttack](https://github.com/thunlp/OpenAttack) - An Open-Source Package for Textual Adversarial Attack. 54 | - [xhlulu / dl-translate](https://github.com/xhlulu/dl-translate) - A deep learning-based translation library built on Huggingface transformers 55 | - [XiaoMi / MiNLP](https://github.com/XiaoMi/MiNLP) - XiaoMi Natural Language Processing Toolkits 56 | 57 | ### 预训练语言模型与Transformer 58 | 相关内容已另行整理至[ATPapers](https://github.com/ZhengZixiang/ATPapers) 59 | 60 | ### 机器翻译 61 | 相关内容已另行整理至[MTPapers](https://github.com/ZhengZixiang/MTPapers) 62 | 63 | ### 命名实体识别与关系抽取 64 | 相关内容已另行整理至[NERPapers](https://github.com/ZhengZixiang/NERPapers) 65 | 66 | ### 机器阅读理解与问答系统 67 | 相关内容已另行整理至[MRCPapers](https://github.com/ZhengZixiang/MRCPapers) 68 | 69 | ### 方面级情感分析 70 | 相关内容已另行整理至[ABSAPapers](https://github.com/ZhengZixiang/ABSAPapers) 71 | 72 | ### 文本匹配 73 | 相关内容已另行整理至[MatchPapers](https://github.com/ZhengZixiang/MatchPapers) 74 | 75 | ### 文本分类 76 | 相关内容已另行整理至[TCPapers](https://github.com/ZhengZixiang/TCPapers) 77 | 78 | ### 文本纠错 79 | - [**pycorrector**](https://github.com/shibing624/pycorrector) - 中文文本纠错工具 80 | 81 | ### 知识图谱 82 | - [**lightKG**](https://github.com/smilelight/lightKG) - 他人基于PyTorch和TorchText实现的知识图谱技术框架 83 | 84 | ### 关键词抽取 85 | - [AdeDZY / DeepCT](https://github.com/AdeDZY/DeepCT) - DeepCT and HDCT uses BERT to generate novel, context-aware bag-of-words term weights for documents and queries 86 | - [AimeeLee77 / keyword_extraction](https://github.com/AimeeLee77/keyword_extraction) - 使用tfidf、TextRank和word2vec实现中文关键词抽取 87 | - [sunyilgdx / SIFRank_zh](https://github.com/sunyilgdx/SIFRank_zh) - 基于预训练模型的中文关键词抽取方法 88 | - [THUNLP / BERT-KPE](https://github.com/thunlp/BERT-KPE) - BERT for Keyphrase Extraction 89 | 90 | ### 依存句法分析 91 | - [Baidu / DDParser](https://github.com/baidu/DDParser) 92 | - [yzhangcs / parser](https://github.com/yzhangcs/parser) - A collection of state-of-the-art syntactic parsing models based on Biaffine Parser 93 | 94 | ### 正则表达式 95 | - [RegExr](https://regexr.com/) - 正则表达式在线学习、测试与分析网站 96 | - [Regex Golf](https://alf.nu/RegexGolf#accesstoken=W0EXx2_lRAMoEeGUVQBx) - 非常好用的经典正则表达式练习网站 97 | 98 | ### 统计自然语言处理工具包 99 | - [Apache OpenNLP](http://opennlp.apache.org/) - Apache开源的Java统计自然语言处理工具包 100 | - [FudanNLP](https://github.com/FudanNLP/fnlp) - 复旦大学开源的统计自然语言处理工具包 101 | - [HTK](http://htk.eng.cam.ac.uk) - 基于马尔可夫模型开发的语音识别工具包 102 | - [Jieba](https://github.com/fxsjy/jieba) - 结巴分词是Python最常用中文分词 103 | - [KenLM](https://kheafield.com/code/kenlm/) - 统计语言模型工具 104 | - [LTP](https://ltp.readthedocs.io/zh_CN/latest/index.html) - 哈工大社会计算与信息检索研究中心开源的统计自然语言处理工具包ji 105 | - [MALLET](http://mallet.cs.umass.edu) - 马萨诸塞大学开源的Java统计自然语言处理工具包 106 | - [NLTK](http://www.nltk.org) - 针对英文的工具包 107 | - [Pan Gu Segment](https://archive.codeplex.com/?p=pangusegment) - 盘古开源中文分词 108 | - [Stanford CoreNLP](https://nlp.stanford.edu/software/) - 斯坦福大学开源的统计自然语言处理工具包 109 | 110 | ### 其他常用工具 111 | - [425776024 / nlpcda](https://github.com/425776024/nlpcda) - 一键中文数据增强包 112 | - [attardi / wikiextractor](https://github.com/attardi/wikiextractor) - A tool for extracting plain text from Wikipedia dumps 113 | - [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc.zh/) - 爬虫常用的HTML和XML数据提取工具 114 | - [Gerapy / GerapyAutoExtractor](https://github.com/Gerapy/GerapyAutoExtractor) - Auto Extractor Module 115 | - [jbesomi / Texthero](https://github.com/jbesomi/texthero) - Text preprocessing, representation and visualization from zero to hero 116 | - [seatgeek / FuzzyWuzzy](https://github.com/seatgeek/fuzzywuzzy) - Fuzzy String Matching in Python 117 | - [SpeechBrain](https://speechbrain.github.io) 118 | - [yongzhuo / Macropodus](https://github.com/yongzhuo/Macropodus) - 自然语言处理工具Macropodus，基于Albert+BiLSTM+CRF深度学习网络架构 119 | 120 | ## 网站与博客 121 | 此处仅提供NLP相关站点，优秀博客请链接[blog.md](https://github.com/ZhengZixiang/nlp_resource/blob/master/blog.md)。 122 | - [AI Deadlines](https://github.com/abhshkdz/ai-deadlines) - AI conference deadline countdowns 123 | - [AI研习社](https://www.yanxishe.com) 124 | - [NLP Progress](https://nlpprogress.com/) 125 | - [NLPJob](http://www.nlpjob.com) 126 | - [专知](https://www.zhuanzhi.ai) 127 | - [智源社区](https://hub.baai.ac.cn/?sort=new) 128 | - [机器之心SOTA模型](https://www.jiqizhixin.com/sota) 129 | 130 | ## 相关团队与实验室 131 | - [Alibaba-NLP](https://github.com/Alibaba-NLP) 132 | - [Chatopera](https://github.com/chatopera) 133 | - [FudanNLP](https://github.com/FudanNLP) 134 | - [NiuTrans](https://github.com/NiuTrans) 135 | - [NJUNLP](https://github.com/NJUNLP) 136 | - [SIAT-NLP](https://github.com/SIAT-NLP) 137 | - [Tencent AI Lab](https://ai.tencent.com/ailab/nlp/) 138 | - [THUDM](https://github.com/THUDM) 139 | - [THUNLP](https://github.com/THUNLP) 140 | 141 | ## 资源集 142 | - 相关内容已另行整理至[awesome_list.md](https://github.com/ZhengZixiang/nlp_resource/blob/master/awesome_list.md) 143 | 144 | ## 竞赛集 145 | - [**CDCS - Chinese Data Competitions Solutions**](https://github.com/geekinglcq/CDCS) - 中国数据竞赛优胜解集锦 146 | - [**AI-Sphere / Awesome-Noah**](https://github.com/AI-Sphere/Awesome-Noah) - Awesome Top Solution List of Excellent AI Competitions 147 | - [**datawhalechina / competition-baseline**](https://github.com/datawhalechina/competition-baseline) - 数据科学竞赛各种baseline代码、思路分享 148 | - [**LogicJake / MLCompetitionHub**](https://github.com/LogicJake/MLCompetitionHub) - Machine learning competition information aggregation 149 | - [**Smilexuhc / Data-Competition-TopSolution**](https://github.com/Smilexuhc/Data-Competition-TopSolution) 150 | - [**zhpmatrix / nlp-competitions-list-review**](https://github.com/zhpmatrix/nlp-competitions-list-review) - 复盘所有NLP比赛的TOP方案 151 | 152 | ## 数据集、语料与常用处理工具资源建设 153 | 相关内容已另行整理至[nlp_corpus.md](https://github.com/ZhengZixiang/nlp_corpus) 154 | --------------------------------------------------------------------------------