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）

Machine Learning Table of Elements Decoded

The Machine Learning periodic table from MLN.io, a machine learning newsletter, lists machine learning packages for languages like Python and Java and tasks like NLP and Computer Vision.
http://www.mln.io/resources/periodic-table/

Bot building platform

自己動手做聊天機器人教程--43篇連載文章
https://github.com/warmheartli/ChatBotCourse
http://www.shareditor.com/bloglistbytag/?tagname=%E8%87%AA%E5%B7%B1%E5%8A%A8%E6%89%8B%E5%81%9A%E8%81%8A%E5%A4%A9%E6%9C%BA%E5%99%A8%E4%BA%BA

機器學習&人工智能博文鏈接匯總
https://www.jianshu.com/p/28f02bb59fe5
[入門問題] 
[機器學習] 
[聊天機器人] 
[好玩兒的人工智能應用實例] 
[TensorFlow] 
[深度學習] 
[強化學習] 
[神經網絡] 
[自然語言處理] 
[數據科學] 

ruyi.ai博客 人人能做机器人
http://blog.ruyi.ai/category/sinews/

What Are Bots?
An overview of the bot landscape

Asking “what are bots?” is kind of like asking “how long is a piece of string?”  It’s a simple question with a complex answer, and we’ll get to that answer by addressing the following questions:

• What are bots?
• What are bots used for?
• Where do bots live?
• Are bots new?
• Why is there so much attention on bots now?
• What’s the business model for bots?
• What does the bot landscape look like?

http://botnerds.com/what-are-bots/


Bot？Chatbot？AI Chatbot？
http://www.high5.ai/blog/bot-chatbot-ai-chatbot

Chatbottle https://chatbottle.co/


Gupshup - Bot building platform 
It enables developers to quickly and easily build, test, deploy and manage chat bots across all messaging channels.

https://www.gupshup.io/developer/home

ChatBot建立教學
Head First ChatBot https://hackmd.io/s/SyJUciYWg
LINE Messaging API https://developers.line.me/
做個 Line Bot 來玩玩 https://blog.ivanwei.co/2017/01/02/2017-01-02-use-line-messaging-api/
IBM 聊天机器人开发指南 https://www.ibm.com/developerworks/cn/cognitive/library/cc-cognitive-chatbot-guide/index.html
Build a Facebook bot without coding https://chatfuel.com/
Line Message API https://kantai235.github.io/2017/03/06/LineMessageAPI/
Facebook Messenger https://kantai235.github.io/2017/02/25/FacebookChatBot/

知識圖譜

知乎
https://www.zhihu.com/topic/19838204/questions

機器學習--超參數相關設定範圍

http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/



http://machinelearningmastery.com/configure-gradient-boosting-algorithm/

Wikipedia Factoid Bot

Post 1: Intro factoid bot demo plus download and configure code
Post 2: Identify famous people as entities using Alchemy Language
Post 3: Initialize the factoid bot’s connection to Watson Conversation
Post 4: You are here
Post 5: Extract answers from DBpedia (Wikipedia)
Post 6: Finalize the conversation flow

Working Demo: Factoid bot running on Bluemix
Source Code: NodeJS factoid bot on Github

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】

Semantic Web vs. semantic technology

Semantic Web Technologies course 
http://www.meteck.org/SWT.html

Foundations of Semantic Web Technologies (許多課程投影片)
http://www.semantic-web-book.org/page/Slides

• Knowledge Representation for the Semantic Web, course at the Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, winter quarter 2012. Covers RDF and OWL in depth.

Transformation of ZML schemas and XML data to RDF/OWL
http://topquadrantblog.blogspot.tw/2011/09/living-in-xml-and-owl-world.html


XML SchemaPlus Specification
This document presents a specification of XML SchemaPlus (XSP) for describing the layout of an XML document in such a way that RDF/OWL semantics can be retained.



Semantic University
http://www.cambridgesemantics.com/semantic-university/semantic-web-vs-semantic-technology#

Some examples of semantic technologies include natural language processing (NLP), data mining, artificial intelligence (AI), category tagging, and semantic search.

Semantic Web technologies include:

• a flexible data model (RDF),
• schema and ontology languages for describing concepts and relationships (RDFS and OWL),
• a query language (SPARQL),
• a rules language (RIF),
• a language for marking up data inside Web pages (RDFa),
• and more.

• Semantic Web Landscape
  • Getting Started: Understanding Semantic Technologies
  • Semantic Technologies Compared
  • Semantic Technologies Applied

• Technical Lesson Tracks
  • Learn RDF
  • Learn OWL and RDFS
  • Learn SPARQL
  • Semantic Web Design Patterns

• About Semantic University
  • Contact Semantic University
  • 
    

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

Notes on Conversational Interfaces

https://quip.com/VjJFAFmzJ35P

Knowledge base systems

Knowledge-based Artificial Intelligence
http://www.mkbergman.com/1816/knowledge-based-artificial-intelligence/
A recent interview with a noted researcher, IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley, provided a downplayed view of recent AI hype. Jordan was particularly critical of AI metaphors to real brain function and took the air out of the balloon about algorithm advances, pointing out that most current methods have roots that are decades long [1]. In fact, the roots of knowledge-based artificial intelligence (KBAI), the subject of this article, also extend back decades.


一些和Knowledge base systems相關的資料整理網站
http://appleiphones.org/images/knowledge+base+systems

A Brief Survey of Ontology Development Methodologies

The Recent Pace of Ontology Development Appears to Have Waned

Some of the leading methodologies, presented in rough order from the oldest to newest, are as follows:

Cyc – this oldest of knowledge bases and ontologies has been mapped to many separate ontologies. See the separate document on the Cyc mapping methodology for an overview of this approach [9]
TOVE (Toronto Virtual Enterprise) – a first-order logic approach to representing activities, states, time, resources, and cost in an enterprise integration architecture [10]
IDEF5 (Integrated Definition for Ontology Description Capture Method) – is part of a broader set of methodologies developed by Knowledge Based Systems, Inc. [11]
ONIONS (ONtologic Integration Of Naive Sources) – a set of methods especially geared to integrating multiple information sources [12], with a particular emphasis on domain ontologies
COINS (COntext INterchange System) – a long-running series of efforts from MIT’s Sloan School of Management [13]
METHONTOLOGY – one of the better known ontology building methodologies; however, not many known uses [14]
OTK (On-To-Knowledge) was a methodology that came from the major EU effort at the beginning of last decade; it is a common sense approach reflected in many ways in other methodologies [15]
UPON (United Process for ONtologies) – is a UML-based approach that is based on use cases, and is incremental and iterative [16].





http://www.mkbergman.com/906/a-brief-survey-of-ontology-development-methodologies/

Some Ongoing KBS/Ontology Projects and Groups

useful Ontology and KR collections
http://www.cs.utexas.edu/users/mfkb/related.html

• Ontology Building Tools - For building and managing ontologies. See Michael Denny's survey of ontology editors (2002), and also the following specific tools:
• Chimaera
• EXPECT
• the Generic Knowledge-Base Editor
• JOE (Java Ontology Editor)
• Jumper (for biological data)
• KAON
• KACTUS
• OntoEdit
• Ontosaurus
• Protege
• Sigma
• Shaken
• Snobase
• VINE - A tool to map ontologies
• WordMap

and additional systems under Knowledge Acquisition Tools.

• Ontology Learning Tools - Automated/assisted techniques for building an ontology. Also see a good survey of ontology learning methods and techniques (OntoWeb deliverable 1.5, A. Gomez-Perez, D. Manzano-Macho).
• The New Ontology of the Mental Causation Debate - an AHRC (Arts & Humanities Research Council) funded research project, attempting to frame the debate with more metaphysical precision, and explore the consequences of that reframing (Univ Durham, UK).
• Ontology Merging Tools - See Chimera and PROMPT. Also see Carter, a tool for helping experts build a consensus KB.
• Ontologies - Dealing with multiple ontologies - See InfoQuilt.

Web Data Semantics and Integration

7  Ontologies, RDF, and OWL
 7.1  Introduction
 7.2  Ontologies by example
 7.3  RDF, RDFS, and OWL
 7.4  Ontologies and (Description) Logics
 7.5  Further reading
 7.6  Exercises
8  Querying Data through Ontologies
 8.1  Introduction
 8.2  Querying RDF data: notation and semantics
 8.3  Querying through RDFS ontologies
 8.4  Answering queries through DL-LITE ontologies
 8.5  Further reading
 8.6  Exercises
9  Data Integration
 9.1  Introduction
 9.2  Containment of conjunctive queries
 9.3  Global-as-view mediation
 9.4  Local-as-view mediation
 9.5  Ontology-based mediators
 9.6  Peer-to-Peer Data Management Systems
 9.7  Further reading
 9.8  Exercices
10  Putting into Practice: Wrappers and Data Extraction with XSLT
 10.1  Extracting Data from Web Pages
 10.2  Restructuring Data
11  Putting into Practice: Ontologies in Practice (by Fabian M. Suchanek)
 11.1  Exploring and installing YAGO
 11.2  Querying YAGO
 11.3  Web access to ontologies
12  Putting into Practice: Mashups with YAHOO! PIPES and XProc
 12.1  YAHOO! PIPES: A Graphical Mashup Editor
 12.2  XProc: An XML Pipeline Language

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

Creating a virtual service desk agent – a DevOps experiment

https://blogs.dxc.technology/2016/05/23/creating-a-virtual-service-desk-agent-a-devops-experiment/


Slightly less rough idea


a final overview of the solution

BigML Releases

https://bigml.com/releases

https://bigml.com/releases/fall-2016
Our Fall 2016 release brings Topic Models, the latest resource that helps you easily find thematically related terms in your text data. Discover BigML’s implementation of the underlying Latent Dirichlet Allocation (LDA) technique, one of the most popular probabilistic methods for topic modeling tasks. This resource is included in our FREE version and it is accessible from the BigML Dashboard as well as the API. Topic Models not only help you better understand and organize your collection of documents, but also can improve the performance of your models for information retrieval tasks, collaborative filtering, or when assessing document similarity.

WhizzML is a new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. WhizzML offers out-of-the-box scalability, abstracts away the complexity of underlying infrastructure, and helps analysts, developers, and scientists reduce the burden of repetitive and time-consuming analytics tasks.

https://bigml.com/releases/spring-2016