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 194 essential critical questions to check off in that domain.
The following domains are covered:
text mining, Commercial software, Part of speech tagging, Security appliance, Information visualization, Full text search, Semantic web, Corpus manager, Copyright Directive, Customer attrition, Research Council, Psychological profiling, Text Analysis Portal for Research, National Institutes of Health, Text clustering, Predictive classification, Exploratory data analysis, Business rule, European Commission, Noun phrase, Lexical analysis, Big data, Predictive analytics, Internet news, Market sentiment, Social sciences, Spam filter, text mining, Record linkage, Document summarization, Concept mining, Information retrieval, Google Book Search Settlement Agreement, Information Awareness Office, Hargreaves review, Information extraction, Ronen Feldman, Fair use, Sentiment Analysis, Text corpus, Business intelligence, National Centre for Text Mining, Scientific discovery, Social media, PubMed Central, Ad serving, Copyright law of Japan, Gender bias, Sequential pattern mining, Machine learning, Biomedical text mining, Structured data, Web mining, Named entity recognition, Document processing, Data mining, Text categorization, Document Type Definition:
text mining Critical Criteria:
Have a round table over text mining results and get the big picture.
– 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?
– Will new equipment/products be required to facilitate text mining delivery for example is new software needed?
– Are assumptions made in text mining stated explicitly?
Commercial software Critical Criteria:
X-ray Commercial software failures and finalize the present value of growth of Commercial software.
– Can Management personnel recognize the monetary benefit of text mining?
– What will drive text mining change?
– How to deal with text mining Changes?
Part of speech tagging Critical Criteria:
Check Part of speech tagging issues and probe using an integrated framework to make sure Part of speech tagging is getting what it needs.
– 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?
– Who will be responsible for making the decisions to include or exclude requested changes once text mining is underway?
– Why should we adopt a text mining framework?
Security appliance Critical Criteria:
Graph Security appliance outcomes and explain and analyze the challenges of Security appliance.
– 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?
– How do we maintain text minings Integrity?
– Is text mining Required?
Information visualization Critical Criteria:
Understand Information visualization tactics and use obstacles to break out of ruts.
– How can we incorporate support to ensure safe and effective use of text mining into the services that we provide?
– How important is text mining to the user organizations mission?
Full text search Critical Criteria:
Check Full text search issues and spearhead techniques for implementing Full text search.
– What are the Essentials of Internal text mining Management?
– Are there text mining problems defined?
– Why are text mining skills important?
Semantic web Critical Criteria:
Disseminate Semantic web planning and gather Semantic web models .
Corpus manager Critical Criteria:
Frame Corpus manager governance and plan concise Corpus manager education.
– Do we monitor the text mining decisions made and fine tune them as they evolve?
– Is a text mining Team Work effort in place?
– Are we Assessing text mining and Risk?
Copyright Directive Critical Criteria:
Be clear about Copyright Directive risks and probe the present value of growth of Copyright Directive.
– Who are the people involved in developing and implementing text mining?
– Which individuals, teams or departments will be involved in text mining?
– Who sets the text mining standards?
Customer attrition Critical Criteria:
Start Customer attrition management and arbitrate Customer attrition techniques that enhance teamwork and productivity.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your text mining processes?
– Does text mining appropriately measure and monitor risk?
– Do we have past text mining Successes?
Research Council Critical Criteria:
Rank Research Council decisions and budget for Research Council challenges.
– Who is the main stakeholder, with ultimate responsibility for driving text mining forward?
– Does text mining analysis isolate the fundamental causes of problems?
– Are there text mining Models?
Psychological profiling Critical Criteria:
Examine Psychological profiling goals and define what do we need to start doing with Psychological profiling.
– How can you measure text mining in a systematic way?
Text Analysis Portal for Research Critical Criteria:
Tête-à-tête about Text Analysis Portal for Research projects and arbitrate Text Analysis Portal for Research techniques that enhance teamwork and productivity.
– How do mission and objectives affect the text mining processes of our organization?
National Institutes of Health Critical Criteria:
Investigate National Institutes of Health governance and report on setting up National Institutes of Health without losing ground.
– What are the disruptive text mining technologies that enable our organization to radically change our business processes?
– What are the success criteria that will indicate that text mining objectives have been met and the benefits delivered?
– How do we ensure that implementations of text mining products are done in a way that ensures safety?
Text clustering Critical Criteria:
Match Text clustering visions and cater for concise Text clustering education.
– Where do ideas that reach policy makers and planners as proposals for text mining strengthening and reform actually originate?
– What are the long-term text mining goals?
Predictive classification Critical Criteria:
Disseminate Predictive classification tasks and devise Predictive classification key steps.
– What are the key elements of your text mining performance improvement system, including your evaluation, organizational learning, and innovation processes?
– What are your most important goals for the strategic text mining objectives?
– What is our text mining Strategy?
Exploratory data analysis Critical Criteria:
Think about Exploratory data analysis tactics and find out what it really means.
– Are there any disadvantages to implementing text mining? There might be some that are less obvious?
Business rule Critical Criteria:
Learn from Business rule visions and change contexts.
– If enterprise data were always kept fully normalized and updated for business rule changes, would any system re-writes or replacement purchases be necessary?
– Does text mining create potential expectations in other areas that need to be recognized and considered?
– Do we all define text mining in the same way?
European Commission Critical Criteria:
Examine European Commission quality and get going.
– Who will be responsible for documenting the text mining requirements in detail?
– How will you know that the text mining project has been successful?
Noun phrase Critical Criteria:
Familiarize yourself with Noun phrase failures and devise Noun phrase key steps.
– How do we make it meaningful in connecting text mining with what users do day-to-day?
– What are internal and external text mining relations?
Lexical analysis Critical Criteria:
Review Lexical analysis failures and report on developing an effective Lexical analysis strategy.
Big data Critical Criteria:
Test Big data planning and point out improvements in Big data.
– From all data collected by your organization, what is approximately the share of external data (collected from external sources), compared to internal data (produced by your operations)?
– What are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?
– Have we let algorithms and large centralized data centres not only control the remembering but also the meaning and interpretation of the data?
– Erp versus big data are the two philosophies of information architecture consistent complementary or in conflict with each other?
– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?
– What is the quantifiable ROI for this solution (cost / time savings / data error minimization / etc)?
– What are some strategies for capacity planning for big data processing and cloud computing?
– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?
– what is needed to build a data-driven application that runs on streams of fast and big data?
– How can the benefits of Big Data collection and applications be measured?
– What is the contribution of subsets of the data to the problem solution?
– Should we be required to inform individuals when we use their data?
– Which Oracle Data Integration products are used in your solution?
– What is/are the corollaries for non-algorithmic analytics?
– What are our tools for big data analytics?
– Isnt big data just another way of saying analytics?
– What if the data cannot fit on your computer?
– What business challenges did you face?
– Are we Using Data To Win?
– What is in Scope?
Predictive analytics Critical Criteria:
Focus on Predictive analytics results and track iterative Predictive analytics results.
– What are direct examples that show predictive analytics to be highly reliable?
– What tools and technologies are needed for a custom text mining project?
– Is the scope of text mining defined?
Internet news Critical Criteria:
Air ideas re Internet news decisions and get out your magnifying glass.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding text mining?
– Why is it important to have senior management support for a text mining project?
Market sentiment Critical Criteria:
Reorganize Market sentiment projects and get going.
– What are the Key enablers to make this text mining move?
– How do we keep improving text mining?
Social sciences Critical Criteria:
Differentiate Social sciences results and triple focus on important concepts of Social sciences relationship management.
– How will we insure seamless interoperability of text mining moving forward?
Spam filter Critical Criteria:
Consider Spam filter governance and research ways can we become the Spam filter company that would put us out of business.
– What are our needs in relation to text mining skills, labor, equipment, and markets?
– When a text mining manager recognizes a problem, what options are available?
– What are the business goals text mining is aiming to achieve?
text mining Critical Criteria:
Drive text mining decisions and spearhead techniques for implementing text mining.
– What is the purpose of text mining in relation to the mission?
– Will text mining deliverables need to be tested and, if so, by whom?
– What is our formula for success in text mining ?
Record linkage Critical Criteria:
Focus on Record linkage tactics and budget for Record linkage challenges.
– Do those selected for the text mining team have a good general understanding of what text mining is all about?
– In what ways are text mining vendors and us interacting to ensure safe and effective use?
Document summarization Critical Criteria:
Design Document summarization governance and be persistent.
– How is the value delivered by text mining being measured?
Concept mining Critical Criteria:
Interpolate Concept mining governance and track iterative Concept mining results.
– 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?
Information retrieval Critical Criteria:
Scan Information retrieval results and look in other fields.
– What knowledge, skills and characteristics mark a good text mining project manager?
– To what extent does management recognize text mining as a tool to increase the results?
Google Book Search Settlement Agreement Critical Criteria:
Prioritize Google Book Search Settlement Agreement projects and find the ideas you already have.
– 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?
Information Awareness Office Critical Criteria:
Reason over Information Awareness Office tactics and handle a jump-start course to Information Awareness Office.
– Have the types of risks that may impact text mining been identified and analyzed?
Hargreaves review Critical Criteria:
Disseminate Hargreaves review failures and reduce Hargreaves review costs.
– What prevents me from making the changes I know will make me a more effective text mining leader?
– What potential environmental factors impact the text mining effort?
Information extraction Critical Criteria:
Unify Information extraction quality and look at the big picture.
– Is maximizing text mining protection the same as minimizing text mining loss?
Ronen Feldman Critical Criteria:
Derive from Ronen Feldman results and devise Ronen Feldman key steps.
– 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?
– Do the text mining decisions we make today help people and the planet tomorrow?
– Is text mining dependent on the successful delivery of a current project?
Fair use Critical Criteria:
Closely inspect Fair use management and modify and define the unique characteristics of interactive Fair use projects.
– Is Supporting text mining documentation required?
Sentiment Analysis Critical Criteria:
Coach on Sentiment Analysis goals and remodel and develop an effective Sentiment Analysis strategy.
– What is the total cost related to deploying text mining, including any consulting or professional services?
– How representative is twitter sentiment analysis relative to our customer base?
Text corpus Critical Criteria:
Participate in Text corpus issues and handle a jump-start course to Text corpus.
– Who will be responsible for deciding whether text mining goes ahead or not after the initial investigations?
– Does our organization need more text mining education?
– How can the value of text mining be defined?
Business intelligence Critical Criteria:
Review Business intelligence goals and know what your objective is.
– Does your BI solution honor distinctions with dashboards that automatically authenticate and provide the appropriate level of detail based on a users privileges to the data source?
– 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?
– What is the difference between Key Performance Indicators KPI and Critical Success Factors CSF in a Business Strategic decision?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– Was your software written by your organization or acquired from a third party?
– What documentation is provided with the software / system and in what format?
– What are typical responsibilities of someone in the role of Business Analyst?
– Does your BI solution help you find the right views to examine your data?
– What social media dashboards are available and how do they compare?
– What is the process of data transformation required by your system?
– Does your BI solution require weeks or months to deploy or change?
– What else does the data tell us that we never thought to ask?
– Can Business Intelligence BI meet business expectations?
– Does your software integrate with active directory?
– Is the product accessible from the internet?
– How stable is it across domains/geographies?
– Can your product map ad-hoc query results?
– What is required to present video images?
– Is your bi software easy to understand?
– What is your expect product life cycle?
National Centre for Text Mining Critical Criteria:
Scrutinze National Centre for Text Mining risks and track iterative National Centre for Text Mining results.
– What is Effective text mining?
Scientific discovery Critical Criteria:
Guide Scientific discovery visions and simulate teachings and consultations on quality process improvement of Scientific discovery.
Social media Critical Criteria:
Chat re Social media tactics and differentiate in coordinating Social media.
– 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 methodology do you use for measuring the success of your social media programs for clients?
– Which of the following are reasons you use social media when it comes to Customer Service?
– Do you have written guidelines for your use of social media and its use by your employees?
– What is our approach to Risk Management in the specific area of social media?
– What is the best way to integrate social media into existing CRM strategies?
– How have you defined R.O.I. from a social media perspective in the past?
– How important is real time for providing social media Customer Service?
– Do you have any proprietary tools or products related to social media?
– Do you offer social media training services for clients?
– Is social media the solution to bad Customer Service?
– Is social media a better investment than SEO?
PubMed Central Critical Criteria:
Deliberate over PubMed Central engagements and check on ways to get started with PubMed Central.
– How can we improve text mining?
Ad serving Critical Criteria:
Audit Ad serving quality and ask questions.
– Is the text mining organization completing tasks effectively and efficiently?
– Are we making progress? and are we making progress as text mining leaders?
Copyright law of Japan Critical Criteria:
Guard Copyright law of Japan failures and be persistent.
Gender bias Critical Criteria:
Scrutinze Gender bias planning and pioneer acquisition of Gender bias systems.
– Think about the functions involved in your text mining project. what processes flow from these functions?
– Are there recognized text mining problems?
Sequential pattern mining Critical Criteria:
Administer Sequential pattern mining engagements and research ways can we become the Sequential pattern mining company that would put us out of business.
Machine learning Critical Criteria:
See the value of Machine learning projects and optimize Machine learning leadership as a key to advancement.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a text mining process. ask yourself: are the records needed as inputs to the text mining process available?
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
Biomedical text mining Critical Criteria:
Dissect Biomedical text mining failures and give examples utilizing a core of simple Biomedical text mining skills.
Structured data Critical Criteria:
Have a meeting on Structured data issues and use obstacles to break out of ruts.
– 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)?
– Does text mining analysis show the relationships among important text mining factors?
– Should you use a hierarchy or would a more structured database-model work best?
Web mining Critical Criteria:
Revitalize Web mining results and create Web mining explanations for all managers.
– How do your measurements capture actionable text mining information for use in exceeding your customers expectations and securing your customers engagement?
– How can you negotiate text mining successfully with a stubborn boss, an irate client, or a deceitful coworker?
Named entity recognition Critical Criteria:
Chart Named entity recognition management and correct Named entity recognition management by competencies.
– Consider your own text mining project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– Is text mining Realistic, or are you setting yourself up for failure?
– Which text mining goals are the most important?
Document processing Critical Criteria:
Investigate Document processing outcomes and oversee Document processing management by competencies.
– What are all of our text mining domains and what do they do?
– What about text mining Analysis of results?
Data mining Critical Criteria:
Guard Data mining strategies and adopt an insight outlook.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this text mining process?
– 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?
– 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?
Text categorization Critical Criteria:
Bootstrap Text categorization engagements and be persistent.
– What are the usability implications of text mining actions?
Document Type Definition Critical Criteria:
Mine Document Type Definition planning and look for lots of ideas.
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.
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 – AbeBooks
Text mining — University of Illinois at Urbana-Champaign
Text Analytics – MeaningCloud text mining solutions
Commercial software External links:
What is commercial software – Answers.com
E-file approved commercial software providers for …
TCR | Commercial Software Submissions
Part of speech tagging External links:
[PDF]STUDY OF PART OF SPEECH TAGGING – ethesis
[PDF]Part of Speech Tagging – BGU
Security appliance External links:
SonicWALL NSA 2600 TotalSecure Security Appliance – CDW.com
http://www.cdw.com › … › Network Security › Firewalls/UTMs
Information visualization External links:
Information visualization (Book, 2001) [WorldCat.org]
Full text search External links:
FDIC: Full Text Search
Semantic web External links:
Marine Metadata Interoperability Project Semantic Web Services
Semantic Web Working Group SPARQL endpoint
Semantic Web Company Home – Semantic Web Company
Corpus manager External links:
Corpus manager – topics.revolvy.com
Virtual Corpus Manager – Archive of Department of …
Copyright Directive External links:
[PDF]Implementing the EU Copyright Directive
Customer attrition External links:
What is customer attrition? | BigCommerce
Customer Attrition Reduction – The Kini Group
Research Council External links:
Social Science Research Council – Official Site
Family Research Council Corporate Portal
The Warehousing Education and Research Council (WERC…
Psychological profiling External links:
Psychological Profiling Flashcards | Quizlet
Psychological Profiling – Introduction
SOLUTION: Psychological Profiling – Law – Studypool
Text Analysis Portal for Research External links:
tapor.ca – TAPoR – Text Analysis Portal for Research
tapor.ca – TAPoR – Text Analysis Portal for Research
TAPoR – Text Analysis Portal for Research | Pearltrees
National Institutes of Health External links:
[PDF]NATIONAL INSTITUTES OF HEALTH
National Library of Medicine – National Institutes of Health
National Institutes of Health – SourceWatch
Text clustering External links:
Text Clustering Case Study – scribd.com
Exploratory data analysis External links:
1. Exploratory Data Analysis
Exploratory Data Analysis | Coursera
Exploratory Data Analysis with R – Leanpub
Business rule External links:
[PDF]Business Rule Number – IRS tax forms
European Commission External links:
European Commission Decision | Antitrust
European Commission Code of Conduct for Data Centre …
European Commission withdraws bank separation proposal
Noun phrase External links:
Grammar Bytes! :: The Noun Phrase
The noun phrase (Book, 2002) [WorldCat.org]
The noun phrase | TeachingEnglish | British Council | BBC
Lexical analysis External links:
Lexical Analysis | The MIT Press
“Lexical Analysis” by Andrew R. Hippisley
Lexical Analysis – University of Mary Washington
Big data External links:
Pepperdata: DevOps for Big Data
Swiftly – Leverage big data to move your city
Qognify: Big Data Solutions for Physical Security & …
Predictive analytics External links:
Customer Analytics & Predictive Analytics Tools for …
Predictive Analytics Solutions & Automated Big Data
Stategic Location Management & Predictive Analytics | …
Internet news External links:
Comprehension and Recall of Internet News: A …
Technology News – New Technology, Internet News, …
Market sentiment External links:
[PDF]Stock Market Sentiment & Technical Indicators
Table Of Contents Table Of ContentsTable Of Contents November 19, 2017 / Stock Market Sentiment & Technical Indicators www.yardeni.com Yardeni Research, Inc.
http://WhisperNumber.com / Market Sentiment LLC
Market Sentiment – Investopedia
Social sciences External links:
Frontpage – Social Sciences
Home – Humanities, Arts and Social Sciences
University of Maryland College of Behavioral and Social Sciences …
Spam filter External links:
TG Spam Filter – Login
Visionary Communications – Spam Filter Login
http://The SpamFilter allows to separate/filter non-spam from spam e-mails prior to downloading them. With the weak filter all e-mails coming from addresses on the “Black list ” go to the spam box. Wit…
text mining External links:
Text Mining – AbeBooks
Text Mining / Text Analytics Specialist – bigtapp
Text Analytics – MeaningCloud text mining solutions
Record linkage External links:
“Record Linkage” by Stasha Ann Bown Larsen
Record linkage (eBook, 1946) [WorldCat.org]
Concept mining External links:
Concept Mining using Conceptual Ontological Graph …
Information retrieval External links:
SIR: Stored Information Retrieval
PPIRS – Past Performance Information Retrieval System
Introduction to Information Retrieval
Google Book Search Settlement Agreement External links:
Google Book Search Settlement Agreement – …
Google Book Search Settlement Agreement – Revolvy
https://www.revolvy.com/topic/Google Book Search Settlement Agreement
Information Awareness Office External links:
Information Awareness Office – SourceWatch
Information extraction External links:
Information extraction — NYU Scholars
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).
[PDF]Information Extraction – CS 452 HOMEPAGE
Ronen Feldman External links:
Author Page for Ronen Feldman :: SSRN
Ronen Feldman – National Bureau of Economic Research
Ronen Feldman’s Phone Number, Email, Address – Spokeo
Fair use External links:
What is fair use? – Definition from WhatIs.com
Stanford Copyright and Fair Use Center
Fair Use | Definition of Fair Use by Merriam-Webster
Sentiment Analysis External links:
YUKKA Lab – Sentiment Analysis
Sentiment Analysis | What is Sentiment Analysis?
SearchBlox – Enterprise Search, Sentiment Analysis, …
Text corpus External links:
North American News Text Corpus – Linguistic Data …
ERIC – A Text Corpus Approach to an Analysis of the …
Business intelligence External links:
business intelligence jobs | Dice.com
List of Business Intelligence Skills – The Balance
National Centre for Text Mining External links:
John McNaught | National Centre for Text Mining | …
National Centre for Text Mining – Revolvy
https://topics.revolvy.com/topic/National Centre for Text Mining
CiteSeerX — National Centre for Text Mining
Scientific discovery External links:
World of scientific discovery (Book, 1994) [WorldCat.org]
Most Popular “Scientific Discovery” Titles – IMDb
[PDF]Scientific Discovery and the Rate of Invention
Social media External links:
WhoDoYou – Local businesses recommended on social media
SOCi Social Media Marketing & Management Platform
Social Media & Dark Web Monitoring | DigitalStakeout
PubMed Central External links:
PubMed Central | Rutgers University Libraries
PubMed Tutorial – Getting the Articles – PubMed Central
MEDLINE, PubMed, and PMC (PubMed Central): How are …
Ad serving External links:
NUI Media – Ad Serving | Digital Media | Development
Powerful Ad Serving Simplified – AdButler
What’s New in Ad Serving Technology | Sovrn
Copyright law of Japan External links:
Copyright Law of Japan | e-Asia
“Copyright law of Japan” on Revolvy.com
https://topics.revolvy.com/topic/Copyright law of Japan
Gender bias External links:
Free gender bias Essays and Papers – 123HelpMe
Title IX and Gender Bias in Language – CourseBB
Most Popular “Gender Bias” Titles – IMDb
Sequential pattern mining External links:
[PDF]Sequential Pattern Mining – Home | College of Computing
Clustering and Sequential Pattern Mining Of Online – …
Machine learning External links:
Microsoft Azure Machine Learning Studio
DataRobot – Automated Machine Learning for Predictive …
Biomedical text mining External links:
[PDF]A Survey of Current Work in Biomedical Text Mining
Biomedical Text Mining Applied To Document Retrieval …
What is Biomedical text mining? – Quora
Structured data External links:
SEC.gov | What Is Structured Data?
Introduction to Structured Data | Search | Google Developers
n4e Ltd Structured Data cabling | Electrical Installations
Web mining External links:
What is Web Mining? – Scale Unlimited
CSE 258 – Recommender Sys&Web Mining – LE [A00] – …
Open Source Web Mining Toolkit | Bixo
Named entity recognition External links:
Named Entity Recognition – thoughtbot
“NAMED ENTITY RECOGNITION AND CLASSIFICATION …
Create an OpenNLP model for Named Entity Recognition …
Document processing External links:
Document Outsourcing | Document Processing | Novitex
Careers Center – Document Processing
LINGO – Web Based EDI Document Processing
Data mining External links:
Data Mining : the Textbook (eBook, 2015) [WorldCat.org]
[PDF]Data Mining Mining Text Data – tutorialspoint.com
[PDF]Data Mining Report – Federation of American Scientists
Text categorization External links:
[PPT]Text Categorization With Support Vector Machines: …
[PDF]Title: Text Categorization for an Online Tendering …
Title: A Text Categorization Approach for Match-Making …
Document Type Definition External links:
[PDF]Document Type Definitions – Mathematical Sciences …
[PDF]Document Type Deﬁnition (DTD) – perfectxml.com