What is involved in Text Analytics

Find out what the related areas are that Text Analytics 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 Analytics thinking-frame.

How far is your company on its Text Analytics journey?

Take this short survey to gauge your organization’s progress toward Text Analytics 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 Analytics related domains to cover and 208 essential critical questions to check off in that domain.

The following domains are covered:

Text Analytics, National Security, PubMed Central, Document processing, Content analysis, Business rule, Limitations and exceptions to copyright, Information retrieval, Text mining, Customer relationship management, Part of speech tagging, Competitive Intelligence, Business intelligence, Tribune Company, Copyright law of Japan, Semantic web, Spam filter, National Diet Library, Text Analysis Portal for Research, Sentiment Analysis, European Commission, Document summarization, Information visualization, Scientific discovery, Ad serving, Big data, Customer attrition, Name resolution, Psychological profiling, National Institutes of Health, Ronen Feldman, UC Berkeley School of Information, Text Analytics, Structured data, Text corpus, Commercial software, Information Awareness Office, News analytics, Pattern recognition, Plain text, National Centre for Text Mining, Corpus manager, Text categorization, Joint Information Systems Committee, Predictive analytics, Open source, Sequential pattern mining, Copyright Directive, Document Type Definition, Gender bias, Exploratory data analysis, Noun phrase, Full text search, Google Book Search Settlement Agreement, Biomedical text mining, Record linkage, Research Council, Internet news:

Text Analytics Critical Criteria:

Match Text Analytics management and give examples utilizing a core of simple Text Analytics skills.

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

– Have text analytics mechanisms like entity extraction been considered?

– Does our organization need more Text Analytics education?

– Do we all define Text Analytics in the same way?

National Security Critical Criteria:

Probe National Security adoptions and optimize National Security leadership as a key to advancement.

– Who will be responsible for documenting the Text Analytics requirements in detail?

– Is Text Analytics Required?

PubMed Central Critical Criteria:

Huddle over PubMed Central governance and give examples utilizing a core of simple PubMed Central skills.

– What management system can we use to leverage the Text Analytics experience, ideas, and concerns of the people closest to the work to be done?

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

– What are the usability implications of Text Analytics actions?

Document processing Critical Criteria:

Talk about Document processing strategies and simulate teachings and consultations on quality process improvement of Document processing.

– How can we incorporate support to ensure safe and effective use of Text Analytics into the services that we provide?

Content analysis Critical Criteria:

Investigate Content analysis quality and create a map for yourself.

– How do we Improve Text Analytics service perception, and satisfaction?

– Why is Text Analytics important for you now?

– What are our Text Analytics Processes?

Business rule Critical Criteria:

Accumulate Business rule tasks and diversify disclosure of information – dealing with confidential Business rule information.

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

– What about Text Analytics Analysis of results?

– What are current Text Analytics Paradigms?

– Are there Text Analytics Models?

Limitations and exceptions to copyright Critical Criteria:

Powwow over Limitations and exceptions to copyright issues and inform on and uncover unspoken needs and breakthrough Limitations and exceptions to copyright results.

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

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Text Analytics?

– Have all basic functions of Text Analytics been defined?

Information retrieval Critical Criteria:

Shape Information retrieval quality and oversee Information retrieval requirements.

– Are there any disadvantages to implementing Text Analytics? There might be some that are less obvious?

Text mining Critical Criteria:

Huddle over Text mining adoptions and look in other fields.

– What are the record-keeping requirements of Text Analytics activities?

– Is a Text Analytics Team Work effort in place?

Customer relationship management Critical Criteria:

Mix Customer relationship management planning and diversify by understanding risks and leveraging Customer relationship management.

– Customer Service What is the future of CRM with regards to Customer Service five years from now, What Technologies would affect it the most and what trends in Customer Service landscape would we see at that time?

– How to ensure high data availability in mobile computing environment where frequent disconnections may occur because the clients and server may be weakly connected?

– Are there any restrictions within the standard support and maintenance agreement on the number of staff that can request support?

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

– In the case of system downtime that exceeds an agreed-upon SLA, what remedies do you provide?

– Can you make product suggestions based on the customers order or purchase history?

– Will the Tier 3 Exchange support team need access to the respondents CRM tool?

– What is the best way to integrate social media into existing CRM strategies?

– Is there an iphone app for mobile scrm or customer relationship management?

– Have you integrated your call center telephony to your crm application?

– What is the potential value of increasing the loyalty of our customers?

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

– Does the current CRM support communication of Tier 3 requests?

– What were the factors that caused CRM to appear when it did?

– What is the Impact of Social CRM on Customer Support?

– Does the current CRM system contain a Web Portal?

– Can the current CRM track calls by call type?

– Can your customers interact with each other?

– How many open tickets are there?

– Where is the ROI in CRM?

Part of speech tagging Critical Criteria:

Chart Part of speech tagging quality and visualize why should people listen to you regarding Part of speech tagging.

– In the case of a Text Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Text Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Text Analytics project is implemented as planned, and is it working?

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

Competitive Intelligence Critical Criteria:

Canvass Competitive Intelligence projects and remodel and develop an effective Competitive Intelligence strategy.

– Meeting the challenge: are missed Text Analytics opportunities costing us money?

– Can we do Text Analytics without complex (expensive) analysis?

– Are we Assessing Text Analytics and Risk?

Business intelligence Critical Criteria:

Differentiate Business intelligence planning and integrate design thinking in Business intelligence innovation.

– Forget right-click and control+z. mobile interactions are fundamentally different from those on a desktop. does your mobile solution allow you to interact with desktop-authored dashboards using touchscreen gestures like taps, flicks, and pinches?

– Does the software allow users to bring in data from outside the company on-the-flylike demographics and market research to augment corporate data?

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

– Can you easily add users and features to quickly scale and customize to your organizations specific needs?

– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?

– What does a typical data warehouse and business intelligence organizational structure look like?

– Does your software facilitate the setting of thresholds and provide alerts to users?

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

– What are the best BI and reporting tools for embedding in a SaaS application?

– What documentation is provided with the software / system and in what format?

– What specialized bi knowledge does your business have that can be leveraged?

– Does creating or modifying reports or dashboards require a reporting team?

– Does your bi solution require weeks or months to deploy or change?

– What are some best practices for managing business intelligence?

– How would you broadly categorize the different BI tools?

– What are the most efficient ways to create the models?

– Can users easily create these thresholds and alerts?

– Does your software integrate with active directory?

– Do we offer a good introduction to data warehouse?

– How is Business Intelligence related to CRM?

Tribune Company Critical Criteria:

Experiment with Tribune Company risks and transcribe Tribune Company as tomorrows backbone for success.

– What potential environmental factors impact the Text Analytics effort?

– Have you identified your Text Analytics key performance indicators?

– How do we go about Comparing Text Analytics approaches/solutions?

Copyright law of Japan Critical Criteria:

Accumulate Copyright law of Japan failures and reinforce and communicate particularly sensitive Copyright law of Japan decisions.

– What prevents me from making the changes I know will make me a more effective Text Analytics leader?

– What other jobs or tasks affect the performance of the steps in the Text Analytics process?

Semantic web Critical Criteria:

Design Semantic web adoptions and describe the risks of Semantic web sustainability.

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

– Does Text Analytics analysis show the relationships among important Text Analytics factors?

– What are specific Text Analytics Rules to follow?

Spam filter Critical Criteria:

Participate in Spam filter management and diversify disclosure of information – dealing with confidential Spam filter information.

– Will Text Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?

– In what ways are Text Analytics vendors and us interacting to ensure safe and effective use?

National Diet Library Critical Criteria:

Merge National Diet Library risks and intervene in National Diet Library processes and leadership.

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

– Are accountability and ownership for Text Analytics clearly defined?

– What are the long-term Text Analytics goals?

Text Analysis Portal for Research Critical Criteria:

Cut a stake in Text Analysis Portal for Research decisions and report on setting up Text Analysis Portal for Research without losing ground.

– Does Text Analytics systematically track and analyze outcomes for accountability and quality improvement?

– How do we make it meaningful in connecting Text Analytics with what users do day-to-day?

– Risk factors: what are the characteristics of Text Analytics that make it risky?

Sentiment Analysis Critical Criteria:

Investigate Sentiment Analysis projects and look at the big picture.

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

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

European Commission Critical Criteria:

Discourse European Commission projects and maintain European Commission for success.

– Are we making progress? and are we making progress as Text Analytics leaders?

Document summarization Critical Criteria:

Match Document summarization decisions and describe the risks of Document summarization sustainability.

– What business benefits will Text Analytics goals deliver if achieved?

Information visualization Critical Criteria:

Give examples of Information visualization quality and sort Information visualization activities.

– Do several people in different organizational units assist with the Text Analytics process?

– Is the scope of Text Analytics defined?

Scientific discovery Critical Criteria:

Troubleshoot Scientific discovery risks and develop and take control of the Scientific discovery initiative.

– Who are the people involved in developing and implementing Text Analytics?

Ad serving Critical Criteria:

Investigate Ad serving management and prioritize challenges of Ad serving.

Big data Critical Criteria:

Administer Big data engagements and pioneer acquisition of Big data systems.

– If this nomination is completed on behalf of the customer, has that customer been made aware of this nomination in advance of this submission?

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

– What are the disruptive innovations in the middle-term that provide near-term domain leadership?

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

– what is needed to build a data-driven application that runs on streams of fast and big data?

– What would be needed to support collaboration on data sharing across economic sectors?

– What are the ways in which cloud computing and big data can work together?

– How to identify relevant fragments of data easily from a multitude of data sources?

– What are the legal risks in using Big Data/People Analytics in hiring?

– What are the new applications that are enabled by Big Data solutions?

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

– Is data-driven decision-making part of the organizations culture?

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

– Are our business activities mainly conducted in one country?

– How fast can we affect the environment based on what we see?

– How to model context in a computational environment?

– Isnt big data just another way of saying analytics?

– What if the data cannot fit on your computer?

– So how are managers using big data?

– Is Big data different?

Customer attrition Critical Criteria:

Drive Customer attrition tasks and innovate what needs to be done with Customer attrition.

– How do your measurements capture actionable Text Analytics information for use in exceeding your customers expectations and securing your customers engagement?

– Does Text Analytics create potential expectations in other areas that need to be recognized and considered?

Name resolution Critical Criteria:

Incorporate Name resolution risks and modify and define the unique characteristics of interactive Name resolution projects.

– Do we monitor the Text Analytics decisions made and fine tune them as they evolve?

– What tools and technologies are needed for a custom Text Analytics project?

– Are there recognized Text Analytics problems?

Psychological profiling Critical Criteria:

Deliberate over Psychological profiling decisions and find out.

– At what point will vulnerability assessments be performed once Text Analytics is put into production (e.g., ongoing Risk Management after implementation)?

– Do Text Analytics rules make a reasonable demand on a users capabilities?

National Institutes of Health Critical Criteria:

Think carefully about National Institutes of Health visions and handle a jump-start course to National Institutes of Health.

– To what extent does management recognize Text Analytics as a tool to increase the results?

– What is our Text Analytics Strategy?

Ronen Feldman Critical Criteria:

Jump start Ronen Feldman engagements and pioneer acquisition of Ronen Feldman systems.

– What new services of functionality will be implemented next with Text Analytics ?

UC Berkeley School of Information Critical Criteria:

Judge UC Berkeley School of Information strategies and budget for UC Berkeley School of Information challenges.

– Will Text Analytics deliverables need to be tested and, if so, by whom?

– What are the Essentials of Internal Text Analytics Management?

Text Analytics Critical Criteria:

Pay attention to Text Analytics management and test out new things.

– Think about the people you identified for your Text Analytics 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?

– Is the Text Analytics organization completing tasks effectively and efficiently?

– Is there any existing Text Analytics governance structure?

Structured data Critical Criteria:

Value Structured data tasks and get answers.

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

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Text Analytics services/products?

– 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 vendors make products that address the Text Analytics needs?

Text corpus Critical Criteria:

Pilot Text corpus quality and create a map for yourself.

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

– How can the value of Text Analytics be defined?

– Are there Text Analytics problems defined?

Commercial software Critical Criteria:

Rank Commercial software management and ask what if.

Information Awareness Office Critical Criteria:

Systematize Information Awareness Office failures and get the big picture.

– How do we Identify specific Text Analytics investment and emerging trends?

News analytics Critical Criteria:

Apply News analytics adoptions and budget for News analytics challenges.

– How do we ensure that implementations of Text Analytics products are done in a way that ensures safety?

– Are assumptions made in Text Analytics stated explicitly?

Pattern recognition Critical Criteria:

Value Pattern recognition visions and drive action.

– How can you measure Text Analytics in a systematic way?

Plain text Critical Criteria:

Mine Plain text engagements and research ways can we become the Plain text company that would put us out of business.

– Consider your own Text Analytics project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?

– What are our needs in relation to Text Analytics skills, labor, equipment, and markets?

– How does the organization define, manage, and improve its Text Analytics processes?

National Centre for Text Mining Critical Criteria:

Disseminate National Centre for Text Mining decisions and remodel and develop an effective National Centre for Text Mining strategy.

– Is maximizing Text Analytics protection the same as minimizing Text Analytics loss?

Corpus manager Critical Criteria:

Analyze Corpus manager planning and know what your objective is.

Text categorization Critical Criteria:

Cut a stake in Text categorization quality and probe using an integrated framework to make sure Text categorization is getting what it needs.

Joint Information Systems Committee Critical Criteria:

Look at Joint Information Systems Committee projects and adopt an insight outlook.

– Who will be responsible for making the decisions to include or exclude requested changes once Text Analytics is underway?

Predictive analytics Critical Criteria:

Give examples of Predictive analytics strategies and correct Predictive analytics management by competencies.

– Does Text Analytics analysis isolate the fundamental causes of problems?

Open source Critical Criteria:

Model after Open source decisions and get going.

– 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?

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

– 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?

– How do we know that any Text Analytics analysis is complete and comprehensive?

– 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?

– Is there an open source alternative to adobe captivate?

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

– What are the open source alternatives to Moodle?

Sequential pattern mining Critical Criteria:

Derive from Sequential pattern mining engagements and pioneer acquisition of Sequential pattern mining systems.

– Have the types of risks that may impact Text Analytics been identified and analyzed?

Copyright Directive Critical Criteria:

Be responsible for Copyright Directive tasks and report on the economics of relationships managing Copyright Directive and constraints.

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

– Is there a Text Analytics Communication plan covering who needs to get what information when?

Document Type Definition Critical Criteria:

Have a meeting on Document Type Definition issues and assess and formulate effective operational and Document Type Definition strategies.

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

Gender bias Critical Criteria:

Be responsible for Gender bias failures and innovate what needs to be done with Gender bias.

– How is the value delivered by Text Analytics being measured?

Exploratory data analysis Critical Criteria:

Steer Exploratory data analysis quality and slay a dragon.

– Think of your Text Analytics project. what are the main functions?

Noun phrase Critical Criteria:

Discourse Noun phrase management and ask questions.

Full text search Critical Criteria:

Administer Full text search results and display thorough understanding of the Full text search process.

– How will you measure your Text Analytics effectiveness?

– How do we maintain Text Analyticss Integrity?

Google Book Search Settlement Agreement Critical Criteria:

Guard Google Book Search Settlement Agreement risks and display thorough understanding of the Google Book Search Settlement Agreement process.

– Do the Text Analytics decisions we make today help people and the planet tomorrow?

– Who will provide the final approval of Text Analytics deliverables?

Biomedical text mining Critical Criteria:

Value Biomedical text mining visions and check on ways to get started with Biomedical text mining.

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

Record linkage Critical Criteria:

X-ray Record linkage outcomes and test out new things.

Research Council Critical Criteria:

Drive Research Council goals and intervene in Research Council processes and leadership.

– Which individuals, teams or departments will be involved in Text Analytics?

– Does the Text Analytics task fit the clients priorities?

Internet news Critical Criteria:

Accumulate Internet news risks and frame using storytelling to create more compelling Internet news projects.

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

– What are internal and external Text Analytics relations?


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


Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]


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 Analytics External links:

How to Use Text Analytics in Business – Data Informed

Text Analytics — Blogs, Pictures, and more on WordPress

National Security External links:

Y-12 National Security Complex – Official Site

National Security Group, Inc. – Insuring your world.

Careers | Y-12 National Security Complex

PubMed Central External links:

Pubmed Central Submission | NIH Library

Need Images? Try PubMed Central | HSLS Update

PubMed Central – YouTube

Document processing External links:

Title Document Processing Jobs Now Hiring | Snagajob

Careers Center – Document Processing

Careers Center – Document Processing

Content analysis External links:

Content Analysis | Pew Research Center

How to Do Content Analysis | The Classroom | Synonym

Content analysis (Book, 2008) [WorldCat.org]

Business rule External links:

Business Rules | [email protected]

[PDF]Business Rule Number – Internal Revenue Service

Information retrieval External links:

Introduction to Information Retrieval

Information Retrieval – RMIT University

Text mining External links:

Text Mining | Metadata | Portable Document Format

Text Mining in R: A Tutorial – Springboard Blog

Text mining — University of Illinois at Urbana-Champaign

Customer relationship management External links:

Salesnet CRM Solutions | Customer Relationship Management

Oracle – Siebel Customer Relationship Management

Oracle – Siebel Customer Relationship Management

Part of speech tagging External links:


[PDF]Part of Speech Tagging – BGU

Competitive Intelligence External links:

Follow.net | Competitive Intelligence for Internet Marketers

Crayon | Market & Competitive Intelligence Tools

Business intelligence External links:

business intelligence jobs | Dice.com

[PDF]Position Title: Business Intelligence Analyst – ttra

Tribune Company External links:

tribune company Jobs in Huntsville, Alabama | Monster.com

Copyright law of Japan External links:

“Copyright law of Japan” on Revolvy.com
pluto.revolvy.com/topic/Copyright law of Japan


Copyright Law of Japan | e-Asia

Semantic web External links:

Semantic Web Working Group SPARQL endpoint

The Semantic Web – An Overview – YouTube

Semantic Web Company Home – Semantic Web Company

Spam filter External links:

Daystarr Spam Filter :: Login

The Best Spam Filters | Top Ten Reviews

WesTel Systems / Remsen, Iowa / My Account / Spam Filter

National Diet Library External links:

Online Gallery | National Diet Library

National Diet Library | library, Tokyo, Japan | Britannica.com

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

Text Analysis Portal for Research External links:

tapor.ca – TAPoR – Text Analysis Portal for Research

TAPoR: Text Analysis Portal for Research | arts …

tapor.ca – TAPoR – Text Analysis Portal for Research

Sentiment Analysis External links:

YUKKA Lab – Sentiment Analysis

Repustate – Sentiment analysis, social media sentiment …

European Commission External links:

European Commission Code of Conduct for Data Centre …

The European Commission explained – Functioning and …

European commission | World | The Guardian

Information visualization External links:

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

Scientific discovery External links:

World of scientific discovery (Book, 1994) [WorldCat.org]

Most Popular “Scientific Discovery” Titles – IMDb

Scientific discovery. (eBook, 1947) [WorldCat.org]

Ad serving External links:

We do ad serving software right | OrbitSoft

AdSpeed.com – Ad Server, Ad Serving & Banner Ad …

Powerful Ad Serving Simplified – AdButler

Big data External links:

Qognify: Big Data Solutions for Physical Security & …

Pepperdata: DevOps for Big Data

Customer attrition External links:

Listening to Feedback Is How You Fight Customer Attrition

Name resolution External links:

Name Resolution | springerprofessional.de

How to troubleshoot DNS name resolution on the Internet …

CiteSeerX — Name Resolution

Psychological profiling External links:

Psychological Profiling – Introduction

SOLUTION: Psychological Profiling – Law – Studypool

Psychological Profiling – VisualDNA

National Institutes of Health External links:

National Library of Medicine – National Institutes of Health

officeofbudget.od.nih.gov/pdfs/FY08/FY08 COMPLETED/NLM.pdf


Ronen Feldman External links:

Ronen Feldman | Weston Capital Management LLC | …

Author Page for Ronen Feldman :: SSRN

Ronen Feldman – Google Scholar Citations

UC Berkeley School of Information External links:

People | UC Berkeley School of Information

About the UC Berkeley School of Information

[PDF]UC Berkeley School of Information – University of …

Text Analytics External links:

Text Analytics — Blogs, Pictures, and more on WordPress

Structured data External links:

Introduction to Structured Data | Search | Google Developers

n4e Ltd Structured Data cabling | Electrical Installations

[PDF]Efficient Population of Structured Data Forms for …

Text corpus External links:

Full-Text Corpus | Nickels and Dimes

Commercial software External links:

efile with Commercial Software | Internal Revenue Service

Commercial Software Assessment Guideline | …

What is commercial software – Answers.com

Information Awareness Office External links:

Information Awareness Office – SourceWatch

Information Awareness Office – revolvy.com
www.revolvy.com/topic/Information Awareness Office

Pattern recognition External links:

Pattern Recognition – IMDb

Pattern Recognition — Alexander Whitley

Pattern Recognition – Official Site

Plain text External links:

TaskPaper – Plain text to-do lists for Mac

PlainTexter – Plain Text and Snippet Sharing

The Plain Text Project

National Centre for Text Mining External links:

CiteSeerX — National Centre for Text Mining (NaCTeM)

National Centre for Text Mining – Revolvy
topics.revolvy.com/topic/National Centre for Text Mining

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

Corpus manager External links:

Virtual Corpus Manager – Archive of Department of …

Text categorization External links:

[PDF]A Text Categorization Based on a Summarization …

Text categorization – Scholarpedia

Predictive analytics External links:

Stategic Location Management & Predictive Analytics | …

Predictive Analytics Workers Compensation

Best Predictive Analytics Software in 2017 | G2 Crowd

Open source External links:

Bitcoin – Open source P2P money

WhiteSource – Open Source Security and License …

Open source
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.

Sequential pattern mining External links:

CiteSeerX — Sequential Pattern Mining in Multi …

[PDF]Sequential PAttern Mining using A Bitmap …

[PDF]Margin-Closed Frequent Sequential Pattern Mining

Copyright Directive External links:

[PDF]Implementing the EU Copyright Directive

Gender bias External links:

What is Gender Bias – Diversity.com

Most Popular “Gender Bias” Titles – IMDb

Free gender bias Essays and Papers – 123HelpMe

Exploratory data analysis External links:

Exploratory Data Analysis with R | Pluralsight

1. Exploratory Data Analysis

Exploratory Data Analysis with R – Leanpub

Noun phrase External links:

Does the Noun Phrase Accessibility Hierarchy Predict …

Noun Phrase | Definition of Noun Phrase by Merriam-Webster
www.merriam-webster.com/dictionary/noun phrase

The noun phrase | TeachingEnglish | British Council | BBC

Full text search External links:

FDIC: Full Text Search

Google Book Search Settlement Agreement External links:

Google Book Search Settlement Agreement – Revolvy
www.revolvy.com/topic/Google Book Search Settlement Agreement

Record linkage External links:

CiteSeerX — Record Linkage: Current Practice and …

“Record Linkage” by Stasha Ann Bown Larsen

Record linkage (eBook, 1946) [WorldCat.org]

Research Council External links:

The Warehousing Education and Research Council (WERC…

BioPharma Research Council

Family Research Council – SourceWatch

Internet news External links:

Americans believe Internet news most reliable

Internet News, Jun 27 2003 | Video | C-SPAN.org

Categories: Documents