NLP词法、句法、语义、语篇综合系列：

NLP+词法系列（一）︱中文分词技术小结、几大分词引擎的介绍与比较 
NLP+词法系列（二）︱中文分词技术及词性标注研究现状（CIPS2016） 
NLP+句法结构（三）︱中文句法结构研究现状（CIPS2016） 
NLP+语义分析（四）︱中文语义分析研究现状（CIPS2016） 
NLP+语篇分析（五）︱中文语篇分析研究现状（CIPS2016）

機器學習、深度學習與自然語言處理領域推薦的書籍列表

https://segmentfault.com/a/1190000008598352

機器學習

• 2007 - Pattern Recognition And Machine Learning【Book】 : The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
• 2012 - Machine Learning A Probabilistic Perspective 【Book】 : This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability , optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning.
• 2012 -李航:統計方法學：李航老師的這本書偏優化和推倒，推倒相應算法的時候可以參考這本書。
• 2014 - DataScience From Scratch【Book】 : In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
• 2015 - Python Data Science Handbook【Book】 :Jupyter Notebooks for the Python Data Science Handbook
• 2015 - Data Mining, The Textbook【Book】 : This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.
• 2016 -周志華機器學習【Book】：周志華老師的這本書非常適合作為機器學習入門的書籍，書中的例子十分形象且簡單易懂。
• University of Illinois at Urbana-Champaign:Text Mining and Analytics【Course】
• 台大機器學習技法【Course】
• 斯坦福機器學習課程【Course】
• CS224d: Deep Learning for Natural Language Processing【Course】
• Unsupervised Feature Learning and Deep Learning【Course】 :來自斯坦福的無監督特徵學習與深度學習系列教程
• 史上最全的机器学习资料（下）涵蓋24種編程語言的機器學習的框架、庫以及其他相關資料https://my.oschina.net/freegodly/blog/740027 

深度學習

• 2015-The Deep Learning Textbook【Book】 :中文譯本這裡，The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.
• Stanford Deep Learning Tutorial【Book】 : This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself , and learn how to apply/adapt these ideas to new problems.
• Neural Networks and Deep Learning【Book】 : Neural Networks and Deep Learning is a free online book. The book will teach you about: (1) Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. (2) Deep learning, a powerful set of techniques for learning in neural networks
• Practical Deep Learning For Coders 【Course】 :七週的免費深度學習課程，學習如何構建那些優秀的模型。
• Oxford Deep NLP 2017 course【Course】 : This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence.

自然語言處理

• 2016 - CS224d: Deep Learning for Natural Language Processing【Course】
• 2017 - Oxford Deep NLP 2017 course【Course】
• [2015 - Text Data Management and Analysis【Book】](): A Practical Introduction to Information Retrieval and Text Mining
• DL4NLP-Deep Learning for NLP resources【Resource】

Deep Learning for Named Entity Recognition

some interesting recent (2015-2016) papers related to that:

http://deeplearningkit.org/2016/04/23/deep-learning-for-named-entity-recognition/

1. Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks – authors: M Francis
2. Entity Attribute Extraction from Unstructured Text with Deep Belief Network – authors: B Zhong, L Kong, J Liu
3. Learning Word Segmentation Representations to Improve Named Entity Recognition for Chinese Social Media – authors: N Peng, M Dredze
4. Biomedical Named Entity Recognition based on Deep Neutral Network – authors: L Yao, H Liu, Y Liu, X Li, MW Anwar
5. Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition – authors: T Baldwin, MC de Marneffe, B Han, YB Kim, A Ritter…
6. Semi-Supervised Approach to Named Entity Recognition in Spanish Applied to a Real-World Conversational System – authors: SS Bojórquez, VM González
7. Boosting Named Entity Recognition with Neural Character Embeddings – authors: C dos Santos, V Guimaraes, RJ Niterói, R de Janeiro
8. Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text – authors: L Li, L Jin, D Huang
9. Entity-centric search: querying by entities and for entities – authors: M Zhou
10. Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach – authors: X Ren, A El
11. Boosting Named Entity Recognition with Neural Character Embeddings – authors: CN Santos, V Guimarães
12. Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. – authors: Y Wu, M Jiang, J Lei, H Xu
13. Context-aware Entity Morph Decoding – authors: B Zhang, H Huang, X Pan, S Li, CY Lin, H Ji, K Knight…
14. Training word embeddings for deep learning in biomedical text mining tasks – authors: Z Jiang, L Li, D Huang, L Jin
15. Entity Attribute Extraction from Unstructured Text with Deep Belief Network – authors: B Zhong, L Kong, J Liu
16. Building Text-mining Framework for Gene-Phenotype Relation Extraction using Deep Leaning – authors: D Jang, J Lee, K Kim, D Lee
17. Text Mining in Social Media for Security Threats – authors: D Inkpen
18. Text Understanding from Scratch – authors: X Zhang, Y LeCun
19. Syntax-based Deep Matching of Short Texts – authors: M Wang, Z Lu, H Li, Q Liu
20. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks – authors: J Tang, M Qu, Q Mei
21. Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach – authors: X Ren, A El
22. Domain-Specific Semantic Relatedness from Wikipedia Structure: A Case Study in Biomedical Text – authors: A Sajadi, EE Milios, V Kešelj, JCM Janssen
23. Deep Unordered Composition Rivals Syntactic Methods for Text Classification – authors: M Iyyer, V Manjunatha, J Boyd
24. Representing Text for Joint Embedding of Text and Knowledge Bases – authors: K Toutanova, D Chen, P Pantel, H Poon, P Choudhury…
25. In Defense of Word Embedding for Generic Text Representation – authors: G Lev, B Klein, L Wolf

Agile Knowledge Engineering and Semantic Web (AKSW)

一個有關semantic web和knowledge engineering的研究群，http://aksw.org/About.html

hosted by the Chair of Business Information Systems (BIS) of the Institute of Computer Science (IfI) / University of Leipzig as well as the Institute for Applied Informatics (InfAI).

Goals

• Development of methods, tools and applications for adaptive Knowledge Engineering in the context of the Semantic Web
• Research of underlying Semantic Web technologies and development of fundamental Semantic Web tools and applications
• Maturation of strategies for fruitfully combining the Social Web paradigms with semantic knowledge representation techniques

AKSW is committed to the free software, open source, open access and open knowledge movements.

Groups


The following subgroups belong to AKSW

• Adaptive Information and Knowledge Engineering
• Agile collaborative requirements engineering
• Creation and evolution of knowledge bases from legacy databases
• Software product-line engineering
• Vocabulary alignment
• Emergent Semantics
• Agile Knowledge Engineering
• Distributed / Federated Social Networks
• Linked Data
• Semantic Software Engineering
• Semantic Web Infrastructure
• Knowledge Integration and Linked Data Technologies
• Data Engineering
• Data Integration
• Data-driven Artificial Intelligence
• DBpedia
• Knowledge Engineering
• Language Technology
• Machine Learning and Ontology Engineering
• Creating knowledge bases from weakly structured data
• Quality assurance and enhancement in ontologies
• Semi-automatic instance matching
• Supervised Machine Learning in OWL/RDF knowledge bases
• Semantic Abstraction
• Knowledge Access, e.g., keyword-based search, question answering, and interfaces
• Knowledge Extraction, e.g., extraction of RDF and OWL from unstructured data
• Knowledge Integration, e.g., link discovery and linked data fusion
• Knowledge Storage, e.g., federated queries, triple stores
• Knowledge-Driven applications, e.g., industry 4.0, big data, benchmark

An overview of word embeddings models

The C&W model without ranking objective (Collobert et al., 2011)

Classic neural language model (Bengio et al., 2003)

A neural language model (Bengio et al., 2006)

http://blog.aylien.com/overview-word-embeddings-history-word2vec-cbow-glove/

Training and serving NLP models using Spark MLlib

https://www.oreilly.com/ideas/training-and-serving-nlp-models-using-spark-mllib

a basic feature pipeline

a high-level diagram of the main tools

NLP survy

Gate-vs-UIMA-vs-OpenNLP
https://app.assembla.com/spaces/extraction-of-cost-data/wiki/Gate-vs-UIMA-vs-OpenNLP

NLP相關資源
https://handong1587.github.io/deep_learning/2015/10/09/nlp.html

NLP 笔记--徐阿衡

http://www.shuang0420.com/categories/NLP/

NLP 笔记 - Sentiment Analysis
06-01
论文笔记 - Learning to Extract Conditional Knowledge for Question Answering using Dialogue
05-24
NLP 笔记 - Text Summarization
05-10
NLP 笔记 - Machine Translation
05-01
NLP笔记 - NLU之意图分类
04-27
NLP 笔记 - Compositional Semantics
04-13
NLP笔记 - Relation Extraction


Deep Learning for NLP course
oxford: https://github.com/oxford-cs-deepnlp-2017/lectures

The course provides a deep excursion into cutting-edge research in deep learning applied to NLP. The final project will involve training a complex recurrent neural network and applying it to a large scale NLP problem.
http://cs224d.stanford.edu/syllabus.html
http://cs224d.stanford.edu/

How to Generate a Good Word Embedding? (source code)
Folder embedding contains all embedding algorithms we used in this paper.

Folder evaluation contains all evaluation tasks in the paper.
https://github.com/licstar/compare

Blog文章

• Deep Learning in NLP
• 维基百科简体中文语料的获取
• 《How to Generate a Good Word Embedding?》导读

http://licstar.net/archives/category/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86

Using NLP, Machine Learning & Deep Learning Algorithms to Extract Meaning from Text
https://www.infoq.com/presentations/nlp-machine-learning-meaning-text

NLP频道
https://liweinlp.com/?p=29
http://blog.sciencenet.cn/blog-362400-902391.html

立委博士，自然語言處理（NLP）資深架構師，Pinciple Scientist, jd-valley, Netbase前首席科學家，期間指揮團隊研發了18種語言的理解和應用系統。特別是漢語和英語，具有世界一流的分析（parsing）精度，並且做到魯棒、線速，scale up to大數據，語義落地到數據挖掘和問答產品。Cymfony前研發副總，曾榮獲第一屆問答系統第一名（TREC-8 QA Track），並贏得17個美國國防部的信息抽取項目（PI for 17 SBIRs）。立委NLP工作的應用方向包括大數據輿情挖掘、客戶情報、信息抽取、知識圖譜、問答系統、智能助理、語義搜索等等。

Introduction to NLP Architecture
https://www.linkedin.com/pulse/introduction-nlp-architecture-wei-li
   

Introduction to Natural Language Processing (NLP) 2016
The field of study that focuses on the interactions between human language and computers is called Natural Language Processing, or NLP for short. 
http://blog.algorithmia.com/introduction-natural-language-processing-nlp/

自然語言處理常用工具及選擇匯總

NLP Tools (http://www.coli.uni-saarland.de/~csporled/page.php?id=tools)
General

• NLTK: the Natural Language Processing Toolkit
• WEKA: easy to use toolkit to play around with different machine learning algorithms
• CoNLL shared task data: annotated data sets for a number of NLP tasks in a number of languages

Web Crawler
Information Retrieval
Language Identification
Pre-processing (Sentence Splitters, Tokenisers, POS Taggers, Lemmatisers, Morphological Analysers)
Syntactic Analysis, Parsers
Text Mining / Information Extraction
Semantic Analysis
Webpages with further information on NLP resources

Dive Into NLTK (http://textminingonline.com/dive-into-nltk-part-i-getting-started-with-nltk)

NLTK is the most famous Python Natural Language Processing Toolkit, here I will give a detail tutorial about NLTK.

• Part I: Getting Started with NLTK (this article)
• Part II: Sentence Tokenize and Word Tokenize
• Part III: Part-Of-Speech Tagging and POS Tagger
• Part IV: Stemming and Lemmatization
• Part V: Using Stanford Text Analysis Tools in Python
• Part VI: Add Stanford Word Segmenter Interface for Python NLTK
• Part VII: A Preliminary Study on Text Classification
• Part VIII: Using External Maximum Entropy Modeling Libraries for Text Classification
• Part IX: From Text Classification to Sentiment Analysis
• Part X: Play With Word2Vec Models based on NLTK Corpus

Biomedical natural language processing
包含各種語言模型的來源和工具

• Word vectors: vector representations of words
• N-gram counts: counts of word sequence occurrences
• Language models: models of word sequence probabilities

http://bio.nlplab.org/


Awesome Community-Curated NLP List
https://github.com/alvations/awesome-community-curated-nlp

用于自然语言处理的Java或Python
https://gxnotes.com/article/51044.html

A curated list of resources for NLP (Natural Language Processing) for Chinese 中文自然语言处理相关资料
https://github.com/crownpku/awesome-chinese-nlp

1. Chinese NLP Toolkits 中文NLP工具

• Toolkits 综合NLP工具包
• 常用的英文或支持多语言的NLP工具包
• Chinese Word Segment 中文分词
• Information Extraction 信息提取
• QA & Chatbot 问答和聊天机器人

2. Corpus 中文语料
3. Organizations 相关中文NLP组织和会议
4. Learning Materials 学习资料


Natural Language Processing Tools (http://www.phontron.com/nlptools.php)

• Dependency Parser(依存語法分析器)
• Finite State Models（有限狀態模型）
• 一般NLP庫（通用NLP工具）
• Language Modeling（語言模型）
• Machine Learning（機器學習）
• Machine Translation Alignment（機器翻譯對齊）
• Machine Translation Decoder（機器翻譯解碼）
• Machine Translation Evaluation（機器翻譯評測）
• Morphological Analysis（形態分析）
• Phrase Structure Parsing（短語結構語法分析器）
• Pronunciation Estimation（發音估計？）
• Speech Recognition（語音識別）

Open-source NLP software

http://entopix.com/so-you-need-to-understand-language-data-open-source-nlp-software-can-help.html

Python NLTK Tools List for Natural Language Processing (NLP)
http://www.datasciencecentral.com/profiles/blogs/python-nlp-tools

Some of the most popular NLP/NLU platforms

• IBM's Watson Conversation Service
• Microsoft LUIS
• Google Natural Language API
• Wit.ai
• Api.ai
• Alexa Skills Kit
• Recast.AI
• Pat

http://www.cio.com/article/3107609/internet-of-things/these-platforms-will-make-your-bots-language-intelligent.html

Natural Language Processing

https://www.celi.it/en/technology/natural-language-processing/

Steps in NLP
https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_natural_language_processing.htm

other: https://www.tekkkies.com/soft-computing-techniques-and-applications/

Machine Learning vs. Natural Language Processing
https://www.lexalytics.com/lexablog/2012/machine-learning-vs-natural-language-processing-part-1

1 Building Natural Language Generation Systems Robert Dale and Ehud Reiter. - ppt download

1 Building Natural Language Generation Systems Robert Dale and Ehud Reiter. - ppt download

What is Natural Language Generation?

http://resources.narrativescience.com/h/i/124944227-what-is-natural-language-generation