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 182 essential critical questions to check off in that domain.
The following domains are covered:
Text Analytics, UC Berkeley School of Information, Text Analytics, Name resolution, Security appliance, Copyright Directive, Joint Information Systems Committee, Copyright law of Japan, Lexical analysis, Information Awareness Office, Competitive Intelligence, Exploratory data analysis, Sequential pattern mining, Named entity recognition, Content analysis, Hargreaves review, News analytics, Concept mining, Part of speech tagging, Limitations and exceptions to copyright, Ad serving, Ronen Feldman, Predictive classification, Text corpus, Noun phrase, Text Analysis Portal for Research, Scientific discovery, Document summarization, Text clustering, Pattern recognition, Predictive analytics, Biomedical text mining, Tribune Company, Spam filter, Plain text, Sentiment Analysis, National Institutes of Health, Semantic web, Information extraction, Corpus manager, Machine learning, Open access, Customer relationship management, Structured data, Social sciences, Social media, Google Book Search Settlement Agreement, Research Council, Big data, Web mining, Information visualization, National Diet Library, Record linkage, Information retrieval, Text categorization, Gender bias, Full text search, Market sentiment, Commercial software:
Text Analytics Critical Criteria:
Study Text Analytics planning and create a map for yourself.
– Does Text Analytics 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?
– Have text analytics mechanisms like entity extraction been considered?
– Does our organization need more Text Analytics education?
– What are the usability implications of Text Analytics actions?
UC Berkeley School of Information Critical Criteria:
Categorize UC Berkeley School of Information decisions and oversee UC Berkeley School of Information requirements.
– Do those selected for the Text Analytics team have a good general understanding of what Text Analytics is all about?
– What sources do you use to gather information for a Text Analytics study?
– Can Management personnel recognize the monetary benefit of Text Analytics?
Text Analytics Critical Criteria:
Examine Text Analytics adoptions and gather practices for scaling Text Analytics.
– At what point will vulnerability assessments be performed once Text Analytics is put into production (e.g., ongoing Risk Management after implementation)?
– How will we insure seamless interoperability of Text Analytics moving forward?
– What are the Key enablers to make this Text Analytics move?
Name resolution Critical Criteria:
Investigate Name resolution decisions and triple focus on important concepts of Name resolution relationship management.
– What are the disruptive Text Analytics technologies that enable our organization to radically change our business processes?
– Does Text Analytics appropriately measure and monitor risk?
– What is Effective Text Analytics?
Security appliance Critical Criteria:
Contribute to Security appliance results and modify and define the unique characteristics of interactive Security appliance projects.
– What are your results for key measures or indicators of the accomplishment of your Text Analytics strategy and action plans, including building and strengthening core competencies?
– How would one define Text Analytics leadership?
Copyright Directive Critical Criteria:
Mix Copyright Directive management and modify and define the unique characteristics of interactive Copyright Directive projects.
– Are there any easy-to-implement alternatives to Text Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– Do we monitor the Text Analytics decisions made and fine tune them as they evolve?
– How do we manage Text Analytics Knowledge Management (KM)?
Joint Information Systems Committee Critical Criteria:
Sort Joint Information Systems Committee tasks and grade techniques for implementing Joint Information Systems Committee controls.
– Meeting the challenge: are missed Text Analytics opportunities costing us money?
– What are the business goals Text Analytics is aiming to achieve?
Copyright law of Japan Critical Criteria:
Apply Copyright law of Japan tasks and acquire concise Copyright law of Japan education.
– What are the top 3 things at the forefront of our Text Analytics agendas for the next 3 years?
– What role does communication play in the success or failure of a Text Analytics project?
– Which Text Analytics goals are the most important?
Lexical analysis Critical Criteria:
Understand Lexical analysis projects and cater for concise Lexical analysis education.
– Is there any existing Text Analytics governance structure?
– How can skill-level changes improve Text Analytics?
Information Awareness Office Critical Criteria:
Reason over Information Awareness Office results and catalog Information Awareness Office activities.
– What are our needs in relation to Text Analytics skills, labor, equipment, and markets?
– What new services of functionality will be implemented next with Text Analytics ?
– What are specific Text Analytics Rules to follow?
Competitive Intelligence Critical Criteria:
Scrutinze Competitive Intelligence strategies and shift your focus.
– Are assumptions made in Text Analytics stated explicitly?
– Does the Text Analytics task fit the clients priorities?
– Is Text Analytics Required?
Exploratory data analysis Critical Criteria:
Confer over Exploratory data analysis governance and correct Exploratory data analysis management by competencies.
– What is the source of the strategies for Text Analytics strengthening and reform?
– What is our Text Analytics Strategy?
Sequential pattern mining Critical Criteria:
Exchange ideas about Sequential pattern mining goals and acquire concise Sequential pattern mining education.
– Are there recognized Text Analytics problems?
– Are there Text Analytics Models?
Named entity recognition Critical Criteria:
Paraphrase Named entity recognition tasks and look at the big picture.
– What are the key elements of your Text Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?
– Who will be responsible for documenting the Text Analytics requirements in detail?
Content analysis Critical Criteria:
Scrutinze Content analysis issues and get going.
– How do you determine the key elements that affect Text Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
– What are your most important goals for the strategic Text Analytics objectives?
– How do we go about Securing Text Analytics?
Hargreaves review Critical Criteria:
Graph Hargreaves review projects and adjust implementation of Hargreaves review.
– How is the value delivered by Text Analytics being measured?
News analytics Critical Criteria:
See the value of News analytics results and adopt an insight outlook.
– What prevents me from making the changes I know will make me a more effective Text Analytics leader?
– Is the Text Analytics organization completing tasks effectively and efficiently?
– What will drive Text Analytics change?
Concept mining Critical Criteria:
Deduce Concept mining quality and devise Concept mining key steps.
– What are the Essentials of Internal Text Analytics Management?
– Why should we adopt a Text Analytics framework?
Part of speech tagging Critical Criteria:
Revitalize Part of speech tagging goals and track iterative Part of speech tagging results.
– To what extent does management recognize Text Analytics as a tool to increase the results?
– What is our formula for success in Text Analytics ?
– What threat is Text Analytics addressing?
Limitations and exceptions to copyright Critical Criteria:
Disseminate Limitations and exceptions to copyright visions and find the essential reading for Limitations and exceptions to copyright researchers.
– What are the long-term Text Analytics goals?
Ad serving Critical Criteria:
Systematize Ad serving projects and gather Ad serving models .
– Can we do Text Analytics without complex (expensive) analysis?
Ronen Feldman Critical Criteria:
Talk about Ronen Feldman management and look at the big picture.
– How do senior leaders actions reflect a commitment to the organizations Text Analytics values?
– In a project to restructure Text Analytics outcomes, which stakeholders would you involve?
– Are there Text Analytics problems defined?
Predictive classification Critical Criteria:
Huddle over Predictive classification visions and question.
– How do we measure improved Text Analytics service perception, and satisfaction?
Text corpus Critical Criteria:
Illustrate Text corpus tasks and triple focus on important concepts of Text corpus relationship management.
– Where do ideas that reach policy makers and planners as proposals for Text Analytics strengthening and reform actually originate?
– Have all basic functions of Text Analytics been defined?
Noun phrase Critical Criteria:
Graph Noun phrase goals and devise Noun phrase key steps.
– Do Text Analytics rules make a reasonable demand on a users capabilities?
Text Analysis Portal for Research Critical Criteria:
Have a session on Text Analysis Portal for Research governance and explain and analyze the challenges of Text Analysis Portal for Research.
– Is Supporting Text Analytics documentation required?
– How will you measure your Text Analytics effectiveness?
Scientific discovery Critical Criteria:
Prioritize Scientific discovery adoptions and customize techniques for implementing Scientific discovery controls.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Text Analytics process?
Document summarization Critical Criteria:
Guard Document summarization results and find answers.
– Why is it important to have senior management support for a Text Analytics project?
– Are accountability and ownership for Text Analytics clearly defined?
Text clustering Critical Criteria:
Reorganize Text clustering projects and devise Text clustering key steps.
– Why are Text Analytics skills important?
Pattern recognition Critical Criteria:
Exchange ideas about Pattern recognition failures and secure Pattern recognition creativity.
– What are the success criteria that will indicate that Text Analytics objectives have been met and the benefits delivered?
– Why is Text Analytics important for you now?
– How do we maintain Text Analyticss Integrity?
Predictive analytics Critical Criteria:
Nurse Predictive analytics decisions and intervene in Predictive analytics processes and leadership.
– 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 direct examples that show predictive analytics to be highly reliable?
Biomedical text mining Critical Criteria:
Add value to Biomedical text mining engagements and shift your focus.
– Who will provide the final approval of Text Analytics deliverables?
Tribune Company Critical Criteria:
Conceptualize Tribune Company governance and optimize Tribune Company leadership as a key to advancement.
– What are your current levels and trends in key measures or indicators of Text Analytics 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 Analytics manager recognizes a problem, what options are available?
Spam filter Critical Criteria:
Generalize Spam filter results and gather practices for scaling Spam filter.
– What are all of our Text Analytics domains and what do they do?
– How do we Lead with Text Analytics in Mind?
Plain text Critical Criteria:
Dissect Plain text strategies and pioneer acquisition of Plain text systems.
– Do the Text Analytics decisions we make today help people and the planet tomorrow?
Sentiment Analysis Critical Criteria:
Exchange ideas about Sentiment Analysis risks and catalog what business benefits will Sentiment Analysis goals deliver if achieved.
– Does Text Analytics analysis show the relationships among important Text Analytics factors?
– How do mission and objectives affect the Text Analytics processes of our organization?
– How representative is twitter sentiment analysis relative to our customer base?
National Institutes of Health Critical Criteria:
Closely inspect National Institutes of Health failures and perfect National Institutes of Health conflict management.
– Will new equipment/products be required to facilitate Text Analytics delivery for example is new software needed?
Semantic web Critical Criteria:
Revitalize Semantic web results and finalize specific methods for Semantic web acceptance.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Text Analytics processes?
– Do several people in different organizational units assist with the Text Analytics process?
Information extraction Critical Criteria:
Revitalize Information extraction tactics and define what our big hairy audacious Information extraction goal is.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Text Analytics process. ask yourself: are the records needed as inputs to the Text Analytics process available?
– Who will be responsible for deciding whether Text Analytics goes ahead or not after the initial investigations?
Corpus manager Critical Criteria:
Scan Corpus manager results and oversee implementation of Corpus manager.
– Will Text Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
Machine learning Critical Criteria:
Have a meeting on Machine learning management and differentiate in coordinating Machine learning.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– What are current Text Analytics Paradigms?
Open access Critical Criteria:
Examine Open access decisions and sort Open access activities.
– How do we Identify specific Text Analytics investment and emerging trends?
– How to deal with Text Analytics Changes?
Customer relationship management Critical Criteria:
Ventilate your thoughts about Customer relationship management leadership and probe Customer relationship management strategic alliances.
– Will the Exchange provide the call volumes and average handle time for the Tier 1 and Tier II calls?
– What IT infrastructure do we have and what do we need to support the future organization needs?
– 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?
– Can visitors and customers opt out of sharing their personal information?
– Can your software be accessed via Windows PCs and Apple Mac computers?
– What are the basic activities of customer life-cycle management?
– Does the user have permission to synchronize the address book?
– The performance measurement revolution: why now and what next?
– How to you ensure compliance with client legal requirements?
– Will the customer have access to a development environment?
– What is your process for gathering business requirements?
– In what way(s) did marketing research help shape CRM?
– Do calls labeled Self Service speak to a CSR?
– How long should e-mail messages be stored?
– Do clients always buy the same thing?
– How much e-mail should be routed?
– What is the client software?
– Who are my customers?
Structured data Critical Criteria:
Reason over Structured data leadership and explore and align the progress in Structured data.
– Which customers cant participate in our Text Analytics domain because they lack skills, wealth, or convenient access to existing solutions?
– 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?
– Do you monitor the effectiveness of your Text Analytics activities?
Social sciences Critical Criteria:
Scan Social sciences adoptions and drive action.
Social media Critical Criteria:
Check Social media outcomes and remodel and develop an effective Social media strategy.
– In the past year, have companies generally improved or worsened in terms of how quickly you feel they respond to you over social media channels surrounding a general inquiry or complaint?
– What is the total cost related to deploying Text Analytics, including any consulting or professional services?
– Are business intelligence solutions starting to include social media data and analytics features?
– What methodology do you use for measuring the success of your social media programs for clients?
– In the past year, have you utilized social media to get a Customer Service response?
– How would our PR, marketing, and social media change if we did not use outside agencies?
– What is our approach to Risk Management in the specific area of social media?
– Do you have any proprietary tools or products related to social media?
– What social media dashboards are available and how do they compare?
– What are the best practices for Risk Management in Social Media?
– How will social media change Category Management and retail?
– Do you offer social media training services for clients?
– How do companies apply social media to Customer Service?
Google Book Search Settlement Agreement Critical Criteria:
Think carefully about Google Book Search Settlement Agreement tactics and drive action.
Research Council Critical Criteria:
Focus on Research Council management and integrate design thinking in Research Council innovation.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Text Analytics models, tools and techniques are necessary?
Big data Critical Criteria:
Revitalize Big data tactics and summarize a clear Big data focus.
– What are the main obstacles that prevent you from having access to all the datasets that are relevant for your organization?
– What is (or would be) the added value of collaborating with other entities regarding data sharing across economic sectors?
– Is the software compatible with new database formats for raw, unstructured, and semi-structured big data?
– What rules and regulations should exist about combining data about individuals into a central repository?
– Do you see areas in your domain or across domains where vendor lock-in is a potential risk?
– Do we understand public perception of transportation service delivery at any given time?
– Technology Drivers – What were the primary technical challenges your organization faced?
– In which way does big data create, or is expected to create, value in the organization?
– Is the process repeatable as we change algorithms and data structures?
– Which Oracle Data Integration products are used in your solution?
– With more data to analyze, can Big Data improve decision-making?
– What is it that we don t know we don t know about the data?
– What is/are the corollaries for non-algorithmic analytics?
– Is recruitment of staff with strong data skills crucial?
– What happens if/when no longer need cognitive input?
– What is collecting all this data?
– what is Different about Big Data?
– Does Big Data Really Need HPC?
Web mining Critical Criteria:
Inquire about Web mining governance and acquire concise Web mining education.
– What will be the consequences to the business (financial, reputation etc) if Text Analytics does not go ahead or fails to deliver the objectives?
– What are the short and long-term Text Analytics goals?
Information visualization Critical Criteria:
Guard Information visualization decisions and raise human resource and employment practices for Information visualization.
– What are our best practices for minimizing Text Analytics project risk, while demonstrating incremental value and quick wins throughout the Text Analytics project lifecycle?
National Diet Library Critical Criteria:
Study National Diet Library projects and plan concise National Diet Library education.
– What are our Text Analytics Processes?
Record linkage Critical Criteria:
Be clear about Record linkage projects and budget for Record linkage challenges.
Information retrieval Critical Criteria:
Examine Information retrieval decisions and get going.
– what is the best design framework for Text Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– What other jobs or tasks affect the performance of the steps in the Text Analytics process?
– How can we improve Text Analytics?
Text categorization Critical Criteria:
Analyze Text categorization visions and look at the big picture.
Gender bias Critical Criteria:
Talk about Gender bias projects and handle a jump-start course to Gender bias.
– 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?
– What are internal and external Text Analytics relations?
Full text search Critical Criteria:
X-ray Full text search engagements and find the essential reading for Full text search researchers.
Market sentiment Critical Criteria:
Apply Market sentiment management and report on developing an effective Market sentiment strategy.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Text Analytics. How do we gain traction?
– What about Text Analytics Analysis of results?
Commercial software Critical Criteria:
Deliberate over Commercial software quality and assess and formulate effective operational and Commercial software strategies.
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
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.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Text Analytics External links:
Text Analytics – Site Title
Text Mining / Text Analytics Specialist – bigtapp
UC Berkeley School of Information External links:
UC Berkeley School of Information
About the UC Berkeley School of Information
UC Berkeley School of Information – Home | Facebook
Text Analytics External links:
Text analytics software| NICE LTD | NICE
Text Analytics – Site Title
Text Mining / Text Analytics Specialist – bigtapp
Name resolution External links:
Microsoft TCP/IP Host Name Resolution Order
The Cable Guy – The Name Resolution Policy Table
Turn off Multicast Name Resolution – Computerstepbystep
Security appliance External links:
Registering your SonicWall Security Appliance | …
Copyright Directive External links:
[PDF]Implementing the EU Copyright Directive
Copyright law of Japan External links:
Copyright Law of Japan | e-Asia
Lexical analysis External links:
2. Lexical analysis — Python 3.6.4 documentation
[PDF]Lexical Analysis and Lexical Analyzer Generators
Lexical Analysis (Concept && Code) – YouTube
Information Awareness Office External links:
Warning: Information Awareness Office
Information Awareness Office (IAO): How’s This for …
Competitive Intelligence External links:
Proactive Worldwide – Competitive Intelligence …
AdPlexity – The World’s Best Competitive Intelligence …
Exploratory data analysis External links:
1. Exploratory Data Analysis
Exploratory Data Analysis with R – Leanpub
Exploratory Data Analysis With R – Online Course | Udacity
Sequential pattern mining External links:
[PDF]Sequential PAttern Mining using A Bitmap …
[PDF]Comparative Study of Sequential Pattern Mining Models
[PDF]Sequential Pattern Mining – Home | College of Computing
Named entity recognition External links:
NAMED ENTITY RECOGNITION – Microsoft Corporation
Content analysis External links:
Content analysis: Introduction – UC Davis, Psychology
[PDF]Three Approaches to Qualitative Content Analysis – …
Content analysis (Book, 2016) [WorldCat.org]
Hargreaves review External links:
Rowan Misty Pattern Book by Kim Hargreaves Review – …
News analytics External links:
RavenPack News Analytics – RavenPack
News Analytics | Amareos
News Analytics, Financial News Aggregation, Market …
Limitations and exceptions to copyright External links:
[PDF]Limitations and Exceptions to Copyright and …
Ad serving External links:
ZEDO Ad Serving : Login
Ronen Feldman External links:
Prof. Ronen Feldman – huji.ac.il
Ronen Feldman | Facebook
Ronen Feldman – Google Scholar Citations
Predictive classification External links:
Predictive classification example with R. Machine …
Text corpus External links:
ERIC – A Text Corpus Approach to an Analysis of the …
TOP 20 Producers. TEXT Corpus to 87778 for free homes …
Noun phrase External links:
Noun Phrase – YouTube
Noun Phrase | Definition of Noun Phrase by Merriam-Webster
BBC Bitesize – What is an expanded noun phrase?
Text Analysis Portal for Research External links:
tapor.ca – TAPoR – Text Analysis Portal for Research
TAPoR: Text Analysis Portal for Research | arts …
Scientific discovery External links:
Scientific discovery (Book, 1990) [WorldCat.org]
World of scientific discovery (Book, 1994) [WorldCat.org]
[PDF]Scientific Discovery and the Rate of Invention
Document summarization External links:
[PDF]Comparison of Multi Document Summarization …
Text clustering External links:
Algorithms for text clustering – Data Science Stack …
Text Clustering Case Study – Scribd
A semantic approach for text clustering using WordNet …
Pattern recognition External links:
Pattern Recognition – Official Site
Pattern Recognition – MATLAB & Simulink – MathWorks
Mike the Knight Potion Practice: Pattern Recognition
Predictive analytics External links:
Predictive Analytics Software, Social Listening | NewBrand
Strategic Location Management & Predictive Analytics | …
Biomedical text mining External links:
What is Biomedical text mining? – Quora
Biomedical text mining and its applications in cancer research
Biomedical Text Mining – Biostar: S
Tribune Company External links:
Tribune Company – The New York Times
Spam filter External links:
Log in – SpamDrain – spam filter for all your devices
The Best Spam Filters | Top Ten Reviews
Web Filter,Internet Site Blocker, Spam Filter & Email Filter
Plain text External links:
Sentiment Analysis External links:
National Institutes of Health External links:
[PDF]National Institutes of Health
[DOC]NATIONAL INSTITUTES OF HEALTH
National Library of Medicine – National Institutes of Health
Semantic web External links:
Semantic Web Flashcards | Quizlet
The Semantic Web – An Overview – YouTube
Semantic Web Company Home – Semantic Web Company
Information extraction External links:
Natural Language Processing and Information Extraction
[PDF]Information Extraction – Brigham Young University
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).
Corpus manager External links:
CiteSeerX — Corpus Manager A Tool for Multilingual …
Corpus manager – Revolvy
Virtual Corpus Manager – Archive of Department of …
Machine learning External links:
Machine Learning | Microsoft Azure
Open access External links:
Directory of Open Access Journals
Open Access on the App Store – iTunes – Apple
SPARC: Advancing Open Access, Open Data, Open …
Customer relationship management External links:
CDK CRM – Automotive Customer Relationship Management
Infor CRM | Customer Relationship Management Software
PipelineDeals – Customer Relationship Management …
Structured data External links:
Structured Data for Dummies – Search Engine Journal
How to Implement Structured Data for SEO – Moz
Structured Data Testing Tool – Google
Social sciences External links:
College of Humanities and Social Sciences
UAH – College of Arts, Humanities, & Social Sciences
Google Book Search Settlement Agreement External links:
Google Book Search Settlement Agreement – …
Research Council External links:
Welding Research Council – Menu
Family Research Council – SourceWatch
National Canine Research Council
Big data External links:
Business Intelligence and Big Data Analytics Software
Big Data Solutions – Amazon Web Services (AWS)
Exam 70-475: Designing and Implementing Big Data …
Web mining External links:
TheWebMiner is a web mining company
Minero – Monero Web Mining
What is Web Mining? – Definition from Techopedia
Information visualization External links:
Information visualization (Book, 2001) [WorldCat.org]
National Diet Library External links:
Online Gallery | National Diet Library
National Diet Library law. (Book, 1961) [WorldCat.org]
Record linkage External links:
[PDF]An Overview of Record Linkage Canada – An official …
Electronic Record Linkage to Identify Deaths Among …
Information retrieval External links:
Introduction to Information Retrieval
PAST PERFORMANCE INFORMATION RETRIEVAL …
PAST PERFORMANCE INFORMATION RETRIEVAL …
Text categorization External links:
[PDF]Title: Text Categorization for an Online Tendering …
What is Text Categorization | IGI Global
[PDF]A Text Categorization Based on a Summarization …
Gender bias External links:
What is Gender Bias? (with pictures) – wiseGEEK
Title IX and Gender Bias in Language – CourseBB
Free gender bias Essays and Papers – 123HelpMe
Full text search External links:
FDIC: Full Text Search
Market sentiment External links:
Delta Tactical Market Sentiment – Barron’s
Market Sentiment – Investopedia
WhisperNumber.com / Market Sentiment LLC
Commercial software External links:
TCR | Commercial Software Submissions
efile with Commercial Software | Internal Revenue Service
E-file-approved commercial software: Personal income tax