NLP 不入门直接放弃

• Online courses
• Libraries and open source
• Active blogs
• Books
• Miscellaneous
• DIY projects and data sets
• NLP on social media

来源：Melanie Tosik（Twitter:@meltomene）列出的 NLP 学习资源清单

#Online courses

• Dan Jurafsky & Chris Manning: Natural Language Processing

• Stanford CS224d: Deep Learning for Natural Language Processing [更高级的机器学习算法、深度学习和 NLP 的神经网络架构]

• Coursera: Introduction to Natural Language Processing [密西根大学的 NLP 课程]

#Libraries and open source

• spaCy (website, blog) [Python；新兴的开放源码库并自带炫酷的用法示例、API 文档和演示应用程序]

• Natural Language Toolkit (NLTK) (website, book) [Python；NLP 实用编程介绍，主要用于教学目的]

• Stanford CoreNLP (website) [由 Java 开发的高质量的自然语言分析工具包]

• AllenNLP (website) [Python；基于 PyTorch 的 NLP 研究库]

• fastText (website) [C++；高效的文本分类（text classification）和表示学习（representation learning）工具]

#Active blogs

• language processing blog （Hal Daumé III）

• Language Log （Mark Liberman）

• Google Research blog

• Explosion AI blog

• Hugging Face

• Sebastian Ruder’s blog

#Books

• Speech and Language Processing （Jurafsky and Martin）[经典的 NLP 教科书，涵盖了所有 NLP 的基础知识，第 3 版即将出版]

• Foundations of Statistical Natural Language Processing （Manning and Schütze）[更高级的统计 NLP 方法]

• Introduction to Information Retrieval （Manning, Raghavan and Schütze）[关于排名/搜索的优秀参考书]

• Neural Network Methods in Natural Language Processing （Goldberg）[深入介绍 NLP 的 NN 方法，和相对应的入门书籍]

• Linguistic Fundamentals for Natural Language Processing （Bender）[更成功的 NLP 的词法和句法]

• Deep Learning （Goodfellow, Courville and Bengio）[很好的深度学习介绍]

#Miscellaneous

• How to build a word2vec model in TensorFlow [学习指南]

• Deep Learning for NLP resources [按主题分类的关于深度学习的顶尖资源的概述]

• Last Words: Computational Linguistics and Deep Learning — A look at the importance of Natural Language Processing. （Manning）[文章]

• Natural Language Understanding with Distributed Representation （Cho）[关于 NLU 的 ML / NN 方法的独立讲义]

• Bayesian Inference with Tears （Knight）[教程工作簿]

• Association for Computational Linguistics （ACL）[期刊选集]

• Quora: How do I learn Natural Language Processing?

• Natural Language Understanding and Computational Semantics （Bowman）[开源的课程大纲和完整幻灯片]

• fast.ai [“Making neural nets uncool again”]

#DIY projects and data sets

Nicolas Iderhoff 已经创建了一份公开、详尽的 NLP 数据集的列表。除了这些，这里还有一些推荐的项目：

• Implement a part-of-speech (POS) tagger (词性标注) based on a hidden Markov model (HMM) (隐马尔可夫模型)

• Implement the CYK algorithm for parsing context-free grammars

• Implement semantic similarity (语义相似度) between two given words in a collection of text, e.g. pointwise mutual information (PMI) (点互信息)

• Implement a Naive Bayes classifier (朴素贝叶斯分类器) to filter spam

• Implement a spell checker based on edit distances between words

• Implement a Markov chain (马尔科夫链) text generator

• Implement a topic model using latent Dirichlet allocation (LDA)

• Use word2vec to generate word embeddings from a large text corpus, e.g. Wikipedia

• Use k-means to cluster tf-idf vectors of text, e.g. news articles

• Implement a named-entity recognizer (NER) (命名实体识别) (also called a name tagger), e.g. following the CoNLL-2003 shared task

#NLP on social media

• Twitter: #nlproc, list of NLPers (by Jason Baldrige)

• Reddit: /r/LanguageTechnology

• Medium: NLP