225 Extremely Powerful text mining Questions You Do Not Know

What is involved in text mining

Find out what the related areas are that text mining connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a text mining thinking-frame.

How far is your company on its text mining journey?

Take this short survey to gauge your organization’s progress toward text mining leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which text mining related domains to cover and 225 essential critical questions to check off in that domain.

The following domains are covered:

text mining, Database Directive, Predictive classification, Intelligence analyst, Biomedical text mining, Named entity recognition, Exploratory data analysis, National Diet Library, Big data, Ronen Feldman, Full text search, text mining, Gender bias, Joint Information Systems Committee, Information visualization, Semantic web, Document Type Definition, Corpus manager, Document summarization, Customer attrition, National Centre for Text Mining, Security appliance, Pattern recognition, Research Council, Copyright Directive, Structured data, Text clustering, Google Book Search Settlement Agreement, PubMed Central, Data mining, UC Berkeley School of Information, Open access, Psychological profiling, Business intelligence, European Commission, Customer relationship management, Hargreaves review, Limitations and exceptions to copyright, Ad serving, Market sentiment, Open source, Copyright law of Japan, Information extraction, National Institutes of Health, Scientific discovery, Name resolution, Internet news, Commercial software, Document processing, Competitive Intelligence, Spam filter, Predictive analytics, Record linkage, Tribune Company, Machine learning, Sentiment Analysis, Noun phrase, Concept mining, Plain text, Web mining, Content analysis, Business rule:

text mining Critical Criteria:

Demonstrate text mining strategies and display thorough understanding of the text mining process.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these text mining processes?

– Where do ideas that reach policy makers and planners as proposals for text mining strengthening and reform actually originate?

Database Directive Critical Criteria:

Confer re Database Directive projects and oversee Database Directive requirements.

– What are the key elements of your text mining performance improvement system, including your evaluation, organizational learning, and innovation processes?

– What sources do you use to gather information for a text mining study?

– Does text mining analysis isolate the fundamental causes of problems?

Predictive classification Critical Criteria:

Study Predictive classification visions and find the ideas you already have.

– What are our best practices for minimizing text mining project risk, while demonstrating incremental value and quick wins throughout the text mining project lifecycle?

– Will text mining deliverables need to be tested and, if so, by whom?

– What about text mining Analysis of results?

Intelligence analyst Critical Criteria:

Understand Intelligence analyst goals and look at it backwards.

– In what ways are text mining vendors and us interacting to ensure safe and effective use?

– What is the difference between a data scientist and a business intelligence analyst?

– What are the key skills a Business Intelligence Analyst should have?

– Can Management personnel recognize the monetary benefit of text mining?

– Are there text mining problems defined?

Biomedical text mining Critical Criteria:

Collaborate on Biomedical text mining risks and do something to it.

– Are assumptions made in text mining stated explicitly?

– How can the value of text mining be defined?

– Are we Assessing text mining and Risk?

Named entity recognition Critical Criteria:

Canvass Named entity recognition projects and raise human resource and employment practices for Named entity recognition.

– What are the top 3 things at the forefront of our text mining agendas for the next 3 years?

Exploratory data analysis Critical Criteria:

Collaborate on Exploratory data analysis leadership and pay attention to the small things.

– Will new equipment/products be required to facilitate text mining delivery for example is new software needed?

– How do we Improve text mining service perception, and satisfaction?

– How to Secure text mining?

National Diet Library Critical Criteria:

Refer to National Diet Library risks and raise human resource and employment practices for National Diet Library.

– Which customers cant participate in our text mining domain because they lack skills, wealth, or convenient access to existing solutions?

– What vendors make products that address the text mining needs?

– Which individuals, teams or departments will be involved in text mining?

Big data Critical Criteria:

Facilitate Big data governance and frame using storytelling to create more compelling Big data projects.

– Do you see the need for actions in the area of standardisation (including both formal standards and the promotion of/agreement on de facto standards) related to your sector?

– New roles. Executives interested in leading a big data transition can start with two simple techniques. First, they can get in the habit of asking What do the data say?

– What are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?

– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?

– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?

– Does your organization perceive the need for more effort to promote security and trust in data technologies?

– What is the quantifiable ROI for this solution (cost / time savings / data error minimization / etc)?

– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?

– Do you see areas in your domain or across domains where vendor lock-in is a potential risk?

– Is senior management in your organization involved in big data-related projects?

– How will systems and methods evolve to remove Big Data solution weaknesses?

– How close to the edge can we push the filtering and compression algorithms?

– What is the contribution of subsets of the data to the problem solution?

– Does your organization have a strategy on big data or data analytics?

– Future Plans What is the future plan to expand this solution?

– How do we measure the efficiency of these algorithms?

– Even when we have a lot of data, do we understand it?

– How do you handle Big Data in Analytic Applications?

– How much data correction can we do at the edges?

– How do we measure value of an analytic?

Ronen Feldman Critical Criteria:

Incorporate Ronen Feldman strategies and look at the big picture.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new text mining in a volatile global economy?

– Are there any easy-to-implement alternatives to text mining? Sometimes other solutions are available that do not require the cost implications of a full-blown project?

– How do senior leaders actions reflect a commitment to the organizations text mining values?

Full text search Critical Criteria:

Revitalize Full text search outcomes and define Full text search competency-based leadership.

– Who is the main stakeholder, with ultimate responsibility for driving text mining forward?

– Who are the people involved in developing and implementing text mining?

– Have all basic functions of text mining been defined?

text mining Critical Criteria:

Confer over text mining tasks and devise text mining key steps.

– What potential environmental factors impact the text mining effort?

– How do we keep improving text mining?

Gender bias Critical Criteria:

Generalize Gender bias failures and display thorough understanding of the Gender bias process.

– Who will be responsible for deciding whether text mining goes ahead or not after the initial investigations?

– What are our text mining Processes?

Joint Information Systems Committee Critical Criteria:

Unify Joint Information Systems Committee results and drive action.

– What new services of functionality will be implemented next with text mining ?

– Are accountability and ownership for text mining clearly defined?

Information visualization Critical Criteria:

Do a round table on Information visualization results and proactively manage Information visualization risks.

– What are the short and long-term text mining goals?

Semantic web Critical Criteria:

Frame Semantic web results and develop and take control of the Semantic web initiative.

– What is Effective text mining?

Document Type Definition Critical Criteria:

Learn from Document Type Definition quality and look in other fields.

– Think about the people you identified for your text mining project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?

– Have the types of risks that may impact text mining been identified and analyzed?

– How do we maintain text minings Integrity?

Corpus manager Critical Criteria:

Examine Corpus manager projects and probe using an integrated framework to make sure Corpus manager is getting what it needs.

– what is the best design framework for text mining organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

– What threat is text mining addressing?

Document summarization Critical Criteria:

Deduce Document summarization risks and get the big picture.

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about text mining. How do we gain traction?

– Does our organization need more text mining education?

– How can skill-level changes improve text mining?

Customer attrition Critical Criteria:

Talk about Customer attrition management and inform on and uncover unspoken needs and breakthrough Customer attrition results.

– What role does communication play in the success or failure of a text mining project?

– Meeting the challenge: are missed text mining opportunities costing us money?

– What are the usability implications of text mining actions?

National Centre for Text Mining Critical Criteria:

Study National Centre for Text Mining strategies and maintain National Centre for Text Mining for success.

– Is there a text mining Communication plan covering who needs to get what information when?

– Why is it important to have senior management support for a text mining project?

– What are the Key enablers to make this text mining move?

Security appliance Critical Criteria:

Interpolate Security appliance governance and finalize specific methods for Security appliance acceptance.

– What tools do you use once you have decided on a text mining strategy and more importantly how do you choose?

– Do several people in different organizational units assist with the text mining process?

Pattern recognition Critical Criteria:

Think about Pattern recognition tactics and research ways can we become the Pattern recognition company that would put us out of business.

– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding text mining?

– Is text mining Realistic, or are you setting yourself up for failure?

– What are the business goals text mining is aiming to achieve?

Research Council Critical Criteria:

Examine Research Council engagements and adopt an insight outlook.

– How does the organization define, manage, and improve its text mining processes?

– Do text mining rules make a reasonable demand on a users capabilities?

Copyright Directive Critical Criteria:

Discourse Copyright Directive engagements and correct Copyright Directive management by competencies.

– What is our text mining Strategy?

Structured data Critical Criteria:

Trace Structured data planning and find out what it really means.

– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?

– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?

– Should you use a hierarchy or would a more structured database-model work best?

– What are specific text mining Rules to follow?

– How do we Lead with text mining in Mind?

Text clustering Critical Criteria:

Give examples of Text clustering engagements and point out Text clustering tensions in leadership.

– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which text mining models, tools and techniques are necessary?

– What are the barriers to increased text mining production?

– Which text mining goals are the most important?

Google Book Search Settlement Agreement Critical Criteria:

Track Google Book Search Settlement Agreement projects and diversify disclosure of information – dealing with confidential Google Book Search Settlement Agreement information.

– What are your current levels and trends in key measures or indicators of text mining product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?

– When a text mining manager recognizes a problem, what options are available?

PubMed Central Critical Criteria:

Focus on PubMed Central tactics and learn.

– Is maximizing text mining protection the same as minimizing text mining loss?

– Does text mining appropriately measure and monitor risk?

Data mining Critical Criteria:

Closely inspect Data mining outcomes and give examples utilizing a core of simple Data mining skills.

– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?

– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?

– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

– What is the difference between business intelligence business analytics and data mining?

– What are our needs in relation to text mining skills, labor, equipment, and markets?

– Is business intelligence set to play a key role in the future of Human Resources?

– What business benefits will text mining goals deliver if achieved?

– What programs do we have to teach data mining?

UC Berkeley School of Information Critical Criteria:

Meet over UC Berkeley School of Information planning and document what potential UC Berkeley School of Information megatrends could make our business model obsolete.

– What are the success criteria that will indicate that text mining objectives have been met and the benefits delivered?

Open access Critical Criteria:

Deduce Open access planning and frame using storytelling to create more compelling Open access projects.

– Are we making progress? and are we making progress as text mining leaders?

Psychological profiling Critical Criteria:

Win new insights about Psychological profiling adoptions and adopt an insight outlook.

– What other jobs or tasks affect the performance of the steps in the text mining process?

– How will you know that the text mining project has been successful?

– How important is text mining to the user organizations mission?

Business intelligence Critical Criteria:

Infer Business intelligence planning and assess what counts with Business intelligence that we are not counting.

– Does the software let users work with the existing data infrastructure already in place, freeing your IT team from creating more cubes, universes, and standalone marts?

– As we develop increasing numbers of predictive models, then we have to figure out how do you pick the targets, how do you optimize the models?

– Which OpenSource ETL tool is easier to use more agile Pentaho Kettle Jitterbit Talend Clover Jasper Rhino?

– How does Tableau stack up against the traditional BI software like Microstrategy or Business Objects?

– What is the biggest value proposition for new BI or analytics functionality at your company?

– Does your bi solution allow analytical insights to happen anywhere and everywhere?

– Social Data Analytics Are you integrating social into your business intelligence?

– What are some software and skills that every Data Scientist should know?

– What social media dashboards are available and how do they compare?

– Does your client support bi-directional functionality with mapping?

– What are some of the hidden costs associated with BI initiatives?

– What type and complexity of system administration roles?

– Can Business Intelligence BI meet business expectations?

– How will marketing change in the next 10 years?

– Where is the business intelligence bottleneck?

– How is Business Intelligence related to CRM?

– Is the product accessible from the internet?

– What is your expect product life cycle?

European Commission Critical Criteria:

Participate in European Commission tasks and explain and analyze the challenges of European Commission.

– How do we go about Securing text mining?

– How can we improve text mining?

Customer relationship management Critical Criteria:

Think carefully about Customer relationship management tactics and remodel and develop an effective Customer relationship management strategy.

– What are 3rd party licenses integrated with the current CRM, for example Email Marketing, Travel Planner, e-newsletter, search engine, surveys, reporting/trend analysis, e-Commerce, etc.?

– Am I making the right decisions related to balancing acquisition, cross-selling and upselling and for the right customer groups?

– Do you really need to store every piece of information you collect (e.g., maybe you just need it one time)?

– Describe what you have found to be the critical success factors for a successful implementation?

– How can we help cultural issues relating to loss of control, constant change and mistrust?

– What is the target level of performance for the Longest delay in Queue KPI?

– Can visitors and customers opt out of sharing their personal information?

– Do you have any proprietary tools or products related to social media?

– What are some of the ways CRM increases our companys revenues?

– Does the user have permission to synchronize the address book?

– How does CRM impact the company s bottom line or performance?

– How do you improve CRM use compliance with your sales team?

– What type of information may be released to whom?

– Do we adhere to best practices interface design?

– Are there multiple Outlook profiles?

– Are we better off going outside?

– Do GM s know what CRM is?

– Who Are Our Customers?

– Is CRM Worth It?

Hargreaves review Critical Criteria:

Sort Hargreaves review governance and define Hargreaves review competency-based leadership.

– What is the total cost related to deploying text mining, including any consulting or professional services?

Limitations and exceptions to copyright Critical Criteria:

Own Limitations and exceptions to copyright engagements and secure Limitations and exceptions to copyright creativity.

– Why should we adopt a text mining framework?

Ad serving Critical Criteria:

Exchange ideas about Ad serving tasks and diversify by understanding risks and leveraging Ad serving.

– How do you determine the key elements that affect text mining workforce satisfaction? how are these elements determined for different workforce groups and segments?

– Does the text mining task fit the clients priorities?

Market sentiment Critical Criteria:

Dissect Market sentiment planning and pay attention to the small things.

– What are your results for key measures or indicators of the accomplishment of your text mining strategy and action plans, including building and strengthening core competencies?

– What other organizational variables, such as reward systems or communication systems, affect the performance of this text mining process?

Open source Critical Criteria:

Detail Open source leadership and test out new things.

– Is there any open source personal cloud software which provides privacy and ease of use 1 click app installs cross platform html5?

– How much do political issues impact on the decision in open source projects and how does this ultimately impact on innovation?

– Think about the functions involved in your text mining project. what processes flow from these functions?

– What are the different RDBMS (commercial and open source) options available in the cloud today?

– Is open source software development faster, better, and cheaper than software engineering?

– Vetter, Infectious Open Source Software: Spreading Incentives or Promoting Resistance?

– What are some good open source projects for the internet of things?

– What are the best open source solutions for data loss prevention?

– Is open source software development essentially an agile method?

– What can a cms do for an open source project?

– Is there an open source alternative to adobe captivate?

– What are the open source alternatives to Moodle?

Copyright law of Japan Critical Criteria:

Match Copyright law of Japan projects and acquire concise Copyright law of Japan education.

– How do we know that any text mining analysis is complete and comprehensive?

– Who sets the text mining standards?

Information extraction Critical Criteria:

Review Information extraction risks and handle a jump-start course to Information extraction.

– Does text mining include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?

National Institutes of Health Critical Criteria:

Face National Institutes of Health goals and clarify ways to gain access to competitive National Institutes of Health services.

– How do we Identify specific text mining investment and emerging trends?

– Who needs to know about text mining ?

Scientific discovery Critical Criteria:

Adapt Scientific discovery governance and intervene in Scientific discovery processes and leadership.

Name resolution Critical Criteria:

Transcribe Name resolution goals and optimize Name resolution leadership as a key to advancement.

Internet news Critical Criteria:

Concentrate on Internet news failures and separate what are the business goals Internet news is aiming to achieve.

Commercial software Critical Criteria:

Troubleshoot Commercial software leadership and look in other fields.

– Think about the kind of project structure that would be appropriate for your text mining project. should it be formal and complex, or can it be less formal and relatively simple?

– Is there any existing text mining governance structure?

– How to deal with text mining Changes?

Document processing Critical Criteria:

Trace Document processing quality and find out.

– What are the long-term text mining goals?

– Are there recognized text mining problems?

Competitive Intelligence Critical Criteria:

Air ideas re Competitive Intelligence issues and pioneer acquisition of Competitive Intelligence systems.

– How do mission and objectives affect the text mining processes of our organization?

Spam filter Critical Criteria:

Brainstorm over Spam filter tasks and intervene in Spam filter processes and leadership.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your text mining processes?

– Think of your text mining project. what are the main functions?

Predictive analytics Critical Criteria:

Canvass Predictive analytics decisions and forecast involvement of future Predictive analytics projects in development.

– What are direct examples that show predictive analytics to be highly reliable?

Record linkage Critical Criteria:

Examine Record linkage adoptions and slay a dragon.

– What will be the consequences to the business (financial, reputation etc) if text mining does not go ahead or fails to deliver the objectives?

– Who will provide the final approval of text mining deliverables?

Tribune Company Critical Criteria:

Give examples of Tribune Company tasks and define what our big hairy audacious Tribune Company goal is.

– How will you measure your text mining effectiveness?

Machine learning Critical Criteria:

Have a round table over Machine learning tasks and test out new things.

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– What is the source of the strategies for text mining strengthening and reform?

Sentiment Analysis Critical Criteria:

Derive from Sentiment Analysis quality and define Sentiment Analysis competency-based leadership.

– For your text mining project, identify and describe the business environment. is there more than one layer to the business environment?

– How likely is the current text mining plan to come in on schedule or on budget?

– How representative is twitter sentiment analysis relative to our customer base?

Noun phrase Critical Criteria:

Be clear about Noun phrase leadership and oversee Noun phrase management by competencies.

– Is the text mining organization completing tasks effectively and efficiently?

– Why are text mining skills important?

Concept mining Critical Criteria:

Gauge Concept mining results and tour deciding if Concept mining progress is made.

Plain text Critical Criteria:

Nurse Plain text planning and spearhead techniques for implementing Plain text.

Web mining Critical Criteria:

Own Web mining outcomes and slay a dragon.

– What knowledge, skills and characteristics mark a good text mining project manager?

– How do we go about Comparing text mining approaches/solutions?

Content analysis Critical Criteria:

Confer over Content analysis decisions and summarize a clear Content analysis focus.

– What are current text mining Paradigms?

– Do we have past text mining Successes?

Business rule Critical Criteria:

Facilitate Business rule results and research ways can we become the Business rule company that would put us out of business.

– If enterprise data were always kept fully normalized and updated for business rule changes, would any system re-writes or replacement purchases be necessary?


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the text mining Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | http://theartofservice.com



Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

text mining External links:

Text Mining Specialist Jobs, Employment | Indeed.com

Text Mining – AbeBooks

Text Mining | Metadata | Portable Document Format

Database Directive External links:

European Union Database Directive – Harvard University

Predictive classification External links:

Predictive classification example with R. Machine …

Intelligence analyst External links:

Military Intelligence Analyst Job Description (MOS 35F)

What does an Intelligence Analyst do?

Intelligence Analyst Salary – PayScale

Biomedical text mining External links:

What is Biomedical text mining? – Quora

Biomedical Text Mining Group

SparkText: Biomedical Text Mining on Big Data Framework.

Named entity recognition External links:

[PDF]A survey of named entity recognition and classification

NAMED ENTITY RECOGNITION – Microsoft Corporation


Exploratory data analysis External links:

Exploratory Data Analysis with R – bookdown

Exploratory Data Analysis With R – Online Course | Udacity

Exploratory Data Analysis with R – Leanpub

National Diet Library External links:

National Diet Library law. (Book, 1961) [WorldCat.org]

Opening Hours & Library Holidays|National Diet Library

Online Gallery | National Diet Library

Big data External links:

Presto | Distributed SQL Query Engine for Big Data

Loudr: Big Data for Music Rights

Databricks – Making Big Data Simple

Ronen Feldman External links:

Ronen Feldman, Ph.D. | Employee Benefit News

Ronen Feldman | Facebook

Ronen Feldman (@RonenF) | Twitter

Full text search External links:

Full Text Search of PDF using Adobe Acrobat

FDIC: Full Text Search

text mining External links:

Text Mining with R

Text mining with MATLAB® (eBook, 2013) [WorldCat.org]

Text Mining / Text Analytics Specialist – bigtapp

Gender bias External links:

Free gender bias Essays and Papers – 123HelpMe

Title IX and Gender Bias in Language – CourseBB

What is Gender Bias – Diversity.com

Information visualization External links:

Information visualization (Book, 2001) [WorldCat.org]

Information visualization (Book, 2017) [WorldCat.org]

Semantic web External links:

Semantic Web Company Home – Semantic Web Company

Semantic Web Flashcards | Quizlet

Content Writing in the Semantic Web | Udemy

Document Type Definition External links:

[PDF]Document Type Definition (DTD) – COE

[PDF]Document Type Definition (DTD) – perfectxml.com

Document Type Definition – Ryte.com

Corpus manager External links:

CiteSeerX — Corpus Manager A Tool for Multilingual …

Corpus manager – Revolvy
https://topics.revolvy.com/topic/Corpus manager&item_type=topic

Virtual Corpus Manager – Archive of Department of …

Document summarization External links:

CiteSeerX — UNL Document Summarization

Document summarization – INTERNATIONAL …

Customer attrition External links:

Listening to Feedback Is How You Fight Customer Attrition

National Centre for Text Mining External links:

National Centre for Text Mining (NaCTeM)

www.Nactem.ac.uk – National Centre for Text Mining — Text

The National Centre for Text Mining (NaCTeM) · GitHub

Security appliance External links:

Cisco Web Security Appliance – Cisco

Registering your SonicWall Security Appliance | …

Buy SonicWALL Tz 100, 01-SSC-8734, Network Security Appliance: Routers – Amazon.com FREE DELIVERY possible on eligible purchases

Pattern recognition External links:

Mike the Knight Potion Practice: Pattern Recognition

Tradable Patterns – Trade Better with Pattern Recognition

Dora’s Ballet Adventure Game: Pattern Recognition – Nick Jr.

Research Council External links:

About Propane | Propane Education & Research Council

Pension Research Council

National Canine Research Council

Copyright Directive External links:

Copyright Directive – WOW.com

[PDF]Implementing the EU Copyright Directive

Structured data External links:

Structured Data for Dummies – Search Engine Journal

What is structured data? – Definition from WhatIs.com

Introduction to Structured Data | Search | Google Developers

Text clustering External links:

Text Clustering Case Study – Scribd

Algorithms for text clustering – Data Science Stack …

Google Book Search Settlement Agreement External links:

Google Book Search Settlement Agreement – …

Topic 6 – The Google Book Search Settlement Agreement

PubMed Central External links:

PubMed Central | NIH Library

Need Images? Try PubMed Central | HSLS Update

PubMed Central (PMC) | NCBI Insights

Data mining External links:

Data mining | computer science | Britannica.com

What is Data Mining in Healthcare?

Nebraska Oil and Gas Conservation Commission – GIS Data Mining

UC Berkeley School of Information External links:

UC Berkeley School of Information – Home | Facebook

About the UC Berkeley School of Information

UNIX Tutorial – UC Berkeley School of Information

Open access External links:

Open Access research and scholarship produced by …

[PDF]SAMPLE Cigna Open Access Plus Plan

Psychological profiling External links:

Psychological profiling – OpenLearn – Open University

Psychological Profiling Flashcards | Quizlet

Business intelligence External links:

CareOregon Business Intelligence

CWS/CMS > Portal > Business Intelligence Portal

Mortgage Business Intelligence Software :: Motivity Solutions

European Commission External links:

European Commission (@EU_Commission) | Twitter

European Commission – PRESS RELEASES Last 7 days

Brazil – Trade – European Commission

Customer relationship management External links:

Oracle – Siebel Customer Relationship Management

Oracle – Siebel Customer Relationship Management

Customer Relationship Management Software | SugarCRM

Limitations and exceptions to copyright External links:

http://shodhganga.inflibnet.ac.in/bitstream/10603/7993/10/10_chapter 5.pdf

Ad serving External links:

Powerful Ad Serving Simplified – AdButler

AdGlare – Ad Serving & Banner Ad Management Software

Market sentiment External links:

Trade Followers – Stock Market Sentiment

Market Sentiment – Investopedia

Delta Tactical Market Sentiment – Barron’s

Open source External links:

WhiteSource – Open Source Security and License …

Open source
http://In production and development, open source as a development model promotes a universal access via a free license to a product’s design or blueprint, and universal redistribution of that design or blueprint, including subsequent improvements to it by anyone. Before the phrase open source became widely adopted, developers and producers used a variety of other terms. Open source gained hold with the rise of the Internet, and the attendant need for massive retooling of the computing source code. Opening the source code enabled a self-enhancing diversity of production models, communication paths, and interactive communities. The open-source software movement arose to clarify the environment that the new copyright, licensing, domain, and consumer issues created. Generally, open source refers to a computer program in which the source code is available to the general public for use and/or modification from its original design. Open-source code is typically a collaborative effort where programmers improve upon the source code and share the changes within the community so that other members can help improve it further.

HR Open Source – Official Site

Copyright law of Japan External links:


Copyright Law of Japan | e-Asia

Copyright law of Japan – WOW.com

Information extraction External links:

Information extraction
http://Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP).

Information extraction (eBook, 2007) [WorldCat.org]

[PDF]Information Extraction – Brigham Young University

National Institutes of Health External links:

[PDF]National Institutes of Health


National Institutes of Health (NIH) — All of Us

Scientific discovery External links:

Grand Challenges – Engineer the Tools of Scientific Discovery

Scientific Discovery Program | St. Cloud State University

Name resolution External links:

[DOC]PDR – Name Resolution without Root Servers

Configuring IP Addressing and Name Resolution

Microsoft TCP/IP Host Name Resolution Order

Internet news External links:

Mobile Internet News Center – Mobile Internet Resource …


Technology News – New Technology, Internet News, …

Commercial software External links:

efile with Commercial Software | Internal Revenue Service

Commercial Software Assessment Guideline | …

E-file approved commercial software providers for …

Document processing External links:

Document Processing Specialist Jobs, Employment | Indeed.com

Document Outsourcing | Document Processing | Novitex

LINGO – Web Based EDI Document Processing

Competitive Intelligence External links:

Strategic and Competitive Intelligence Professionals …

Proactive Worldwide – Competitive Intelligence …

Spam filter External links:

The Best Spam Filters | Top Ten Reviews

Visionary Communications – Spam Filter Login

How to Create an Outlook Junk Email or SPAM Filter

Predictive analytics External links:

Strategic Location Management & Predictive Analytics | …

Predictive Analytics Software, Social Listening | NewBrand

Inventory Optimization for Retail | Predictive Analytics

Record linkage External links:

[PDF]An Overview of Record Linkage Canada – An official …

Electronic Record Linkage to Identify Deaths Among …

Tribune Company External links:

Case Study – Tribune Company – O’Melveny


Tribune Company – The New York Times

Machine learning External links:

DataRobot – Automated Machine Learning for Predictive …

What is machine learning? – Definition from WhatIs.com

Microsoft Azure Machine Learning Studio

Sentiment Analysis External links:

YUKKA Lab – Sentiment Analysis

Sentiment Analysis | What is Sentiment Analysis?

SearchBlox – Enterprise Search, Sentiment Analysis, …

Noun phrase External links:

BBC Bitesize – What is an expanded noun phrase?

Types of Phrases – Noun Phrase, Verb Phrase, Gerund …

Noun Phrase: Examples and Definition – English Sentences

Plain text External links:

Plain Text

GPS Visualizer: Convert GPS files to plain text or GPX

How to Use TextEdit Plain Text Mode by Default in Mac OS X

Web mining External links:

What is Web Mining? – Scale Unlimited

CSE 258 – Recommender Sys&Web Mining – LE [A00] – …

Web Mining • r/Monero – reddit

Content analysis External links:

Vision API – Image Content Analysis | Google Cloud Platform

Content analysis (Book, 2016) [WorldCat.org]

Content analysis: Introduction – UC Davis, Psychology

Business rule External links:

[PDF]Business Rule Number – Internal Revenue Service

Business Rules vs. Business Requirements …

Glossary – Business Rule

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