To contrast our approach to mining unstructured data with others, DataScava commissioned an “Executive Q&A: DataScava, AI and ML” and a series of articles from Scott Spangler, former IBM Watson Health Researcher, Chief Data Scientist, and author of the book “Mining the Talk: Unlocking the Business Value in Unstructured Information.”
Scott discusses how and why DataScava’s patented precise approach to mining unstructured text data perfectly complements real-world big data applications in Talent, AI, ML, RPA, BI, Research, Operations, and more.
In addition, he compares our Domain-Specific Language Processing (DSLP), Weighted Topic Scoring (WTS), and Tailored Topics Taxonomies (TTT) with standard methods such as Natural Language Processing (NLP) and Natural Language Understanding (NLU).
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In this first article in the series, Scott discusses:
- The pitfalls of using a fully automated approach to critical decision-making.
- The desirability of having a parallel human-machine partnership that regulates and monitors the inputs and outputs of automated approaches.
- The three basic ingredients that are needed to make that hybrid process successful and how DataScava implements each of these components.
Here’s an excerpt
“Algorithms will be more effective in the long run if they are part of a more holistic framework that includes user-controlled domain-specific ontologies, statistical analysis, and rule-based reasoning strategies. These are the basic ingredients that a tool like DataScava provides.
DataScava . . .
“Is a robot ally in humanity’s struggle for control of how we utilize big data to make decisions. By providing tools for capturing the key underlying topics and rules that govern important concepts of the business needs, it evens the playing field so that machine learning no longer has to have the final say on critical business decisions.
Can supervise the process based on human-provided expertise and determine which data to use for training and which to avoid, as well as in which situations to trust deep learning decisions and when to fall back on more rule-based approaches. Such processes put the humans back in charge and allow the machines to serve their intended role as adjuncts and trusted advisors.
In partnership with a trained human mind – can act effectively as a tool for giving the left brain an equal say in big data decision-making tasks.
Can play a leading role in helping businesses manage and maintain their big data more efficiently using information ontologies, statistics with visualization and rule-based approaches.
Perfectly complements existing approaches to unlocking the value of unstructured text data – by helping companies to model higher-level intents and purposes behind the labeling and classification of data – by capturing the abstract topics and themes that represent their own business and subject matter expertise – and by applying both to big data sets real-time.
Provides a practical, easy-to-use tool-set for capturing the critical business ontologies that provide the critical bridge between unstructured text data analysis using standard data science techniques and the human expertise that gives your business its competitive edge.
When a deep learning system and DataScava agree on a classification, that’s ideal because then we now have a plausible explanation for why the deep learning algorithm decided the way it did.
Can help data professionals and business people use machine and human intelligence together to make their messy unstructured text data more accessible, understandable and actionable.”
How DataScava’s Domain-Specific Language Processing Mines Value From Unstructured Data
Here’s an excerpt from a guest blog post for big data site KDnuggets written by Janet Dwyer, our CEO, and John Harney, our CTO that explains how DataScava (the unstructured data miner that powers TalentBrowser) uses Domain-Specific Language Processing (DSLP), patented Weighted Topic Scoring (WTS), the language of your business and a customized search engine you control to mine and match your resumes and profiles to your job openings.
“Real-time mining of unstructured textual content isn’t simple. To work effectively, a solution must be fine-tuned to meet your organization’s specific needs and address the quirks in your company’s information. To add value, applications require a vocabulary that accurately captures the definitions, context, and nuance of your business and the way it uses language.
Consequently, unstructured textual data needs to be organized before it can be put to use. That’s a huge challenge. For data-driven systems based on artificial intelligence, machine learning or other advanced applications, natural language processing and natural language understanding are supposed to be the solution, either by themselves or as hybrid models that plug in industry terms or use complex Boolean logic. But none of these are easy to use or implement, and without extensive programming and training, they often process data incorrectly.
There is, however, a simpler approach to addressing these challenges, one that does not require NLP or NLU. It’s called “Domain-Specific Language Processing,” or DSLP, and it uses the language of your business to mine unstructured textual data.”
Evolution of Recruiting: A Search Synopsis
Here’s an excerpt from a blog post written by Janet Dwyer, our CEO:
“How big is your candidate database?”
This is a question people actually used to ask all the time in the recruiting community. Before we had access to millions of data points at a moment’s notice, it was all about the Rolodex, Excel sheets, and folders of your best people – the people who, at a moment’s notice, would take your call about a job. Relationships were a currency and business cards were gold.
Then came the Internet. As we started to collect free data, recruiters eventually caught on. A few people started to figure out how to scrape and that was a game changer. Access to a person’s information wasn’t a premium anymore, but the platform was. Enter the next phase: tools.
Read the full article here: Evolution of Recruiting: A Search Synopsis
Let’s Admit It: We’re a Long Way from using ‘Real Intelligence” in AI
With the growth of AI systems and unstructured data, there is a need for an independent means of data curation, evaluation and measurement of output that does not depend on the natural language constructs of AI and creates a comparative method of how the data is processed.
Here’s an excerpt from a blog post on this subject written by our CTO John Harney published by big data site KDNuggets:
“For anyone worrying about machines taking over the world, I have reassuring news: The idea of artificial intelligence has been overcome by hype. I don’t mean to belittle AI’s promise or even its existing capabilities. The technology allows organizations to put data to use in ways we could only imagine not that long ago.
“It’s revolutionized the way executives approach strategic planning. But very often lately—when I’m in meetings, reading research papers or listening to an expert’s presentation—I can’t shake the feeling that to many people, terms like “AI,” “machine learning” and “cognitive computing” have become answers unto themselves.
“Today, solutions providers put statements like “AI-driven” or “harnessing the power of machine learning” at the core of their sales pitch. The buzzwords are certainly getting through. One colleague tells the story of a client calling “to make sure AI was included” in their data analysis project. Business people have been sold on the notion that today’s cutting-edge systems analyze data in a black box, then spit out reliable insights. How? They just do.”
Read the full article here: Let’s Admit It: We’re a Long Way from Using ‘Real Intelligence’ in AI.
Busting a Buzzword: Semantic Search
TalentBrowser was featured in a piece by the renowned RecruitingTools news blog. Author Katrina Kibben writes:
“What we really need, and I only know one company that does this (shout out to TalentBrowser, powered by DataScava, and founders Janet Dwyer and John Harney) is a completely customizable white box ‘profile’ search built on input and personalized rules that you the user control, not a black box semantic search engine that thinks it knows what you ‘really mean.’ Profile search allows you to specify many individual topics in a search, with thresholds (minimums) to be met by each topic. This twofold process bubbles the best candidates right to the top.”
Click here for the full report.
TalentBrowser Presents at “The Future of HR Tech” Event
TalentBrowser was selected to present at “The Future of HR Tech” HR.com virtual event showcasing startups in the Human Capital Management (HCM) space pitching their innovative products to a global market, helping to shape the future of HR. To view the presentation, click here.
William Tincup: HR Technologies to Watch
In a blog post written by William Tincup, Principal Analyst at KeyInterval Research, TalentBrowser was featured as a top HR technology firm to watch in 2016. Check out the full list here.
The Recruiting Animal: Talking TalentBrowser
February 03, 2016
The Co-Founders of TalentBrowser, Janet Dwyer and John Harney, and Andrew Gadomski, Founder of Aspen Advisors, a recruiting and talent acquisition efficiency advisory firm, joined the podcast “The Recruiting Animal” to discuss TalentBrowser, which Gadomski refers to as “Netflix for Recruiters.” Click on the audio player above to hear the full conversation.
HR Latte: TalentBrowser Taking the “Tiger by the Tail”
The Co-Founders of TalentBrowser, Janet Dwyer and John Harney, as well as Al Mellina, CEO and Managing Partner of Gartland & Mellina, joined Rayanne Thorn,VP – Marketing, Strategist, Writer, and Radio Host at Dovetail Software and HR Latte Talk Radio, to talk about what TalentBrowser can deliver to recruiters as a candidate search technology. What you’ll learn from this segment:
- How TalentBrowser is providing a new alternative to current sourcing solutions.
- The ways that TalentBrowser combines analytics with employee engagement to help organizations make the right hiring decisions.
- Al Mellina discusses their TalentBrowser case study — “The proof is in the results.”
- How TalentBrowser is creating specialized results by using data and analytics the right way.
HR Latte Conversation: Resume Analytics with Janet Dwyer
HR Latte chats with Janet Dwyer, Chief Executive Officer and Co-Founder at TalentBrowser, about resume analytics and how technology benefits sourcing and recruiting new talent. A successful recruiter for many years, managing her own search firm Integretech, Janet and her partner John Harney knew they could make the recruitment process better by creating a patented technology which creates new efficiencies for recruiters.
Long Island Business News: “Cyber Headhunter”
It’s like hiring without a heartbeat. TalentBrowser, a job matching service for hiring managers and recruiters, uses technology as a medium to eliminate weak job applicants and bring the most qualified ones to the forefront of a recruiter’s computer screen. Developed by Kings Park entrepreneur and chief information officer John Harney, the TalentBrowser software uses an algorithm to electronically sift through hundreds to thousands of backlogged resumes stored in a company’s system.
The Shift in Search — TalentBrowser Stops by HR Latte
Founders of TalentBrowser, Janet Dwyer and John Harney stop by HR Latte to talk about what they are up to and why TalentBrowser will change database search. Initially founded to support their successful IT in financial recruitment practice, Integretech, Dwyer and Harney built a search solution which allowed them to step outside the bounds of keyword search, to provide the best “resume to job” matching tech available today.
Startup Spotlight: TalentBrowser Scores and Matches
What problem are you trying to solve?
Missing the best candidate. With every resume being different, it’s a huge challenge to effectively sort and filter through incoming resumes, let alone search the existing database which has to be considered. Truly knowing which candidate is the best fit for skills is a monumental task that is only possible with the assistance of a world class search and match tool. TalentBrowser is this tool. An executive at a leading ATS company described the problem best: “The reality is that no ATS does a good job in search or match. Ours is the best, but it’s not very good either! We’re a mile wide but just an inch deep. I would describe TalentBrowser not as an ATS, but as a premier search engine.”