Entity extraction is a core capability of text analytics, so I thought I’d step through an example of how it works.
Note: Learn about the all the different text analytics capabilities in my last post Text Analytics: A Spectrum of Enrichment Techniques.
Consider the following text that talks about Apple and Berkshire Hathaway. Entity extraction lets you pick out the entities in the text (companies, persons, locations) and then help you understand how they are related to other.
For cognitive search to work, you need text analytics, which is why it’s a key component of the Attivio Cognitive Search Platform. There is a range of capabilities within text analytics to understand, so I thought I’d take you through them and explain how they work.
From Directed to Discovery
Text analytics is the process of extracting valuable information from text-based content - or unstructured content - for business purposes. It seems simple, but it’s far from it.
These days, there’s pretty much a trade show for everything — and predictive analytics is no exception. The most recent stateside edition of Predictive Analytics World™ took place in Chicago in June of this year. And predictive maintenance occupied a prominent place on the agenda. One presentation concerned failure and fault detection for Rolls Royce.
There was a time when predictive maintenance encompassed only the analysis of structured data. In 2013, a blogger on the data science website Simafore suggested four ways predictive analytics could improve equipment maintenance: trend analysis, pattern recognition, critical range and limits, and statistical process analysis. Conspicuously absent in the discussion of the tools, techniques, and data types that supported predictive analytics was unstructured information and text analytics.
A recent study by IDC, Data Age 2025: The Evolution of Data to Life-Critical, projects that the amount of data subject to analysis will grow by a factor of 50 between now and 2025. Further, the amount of analyzed data affected by cognitive systems will grow by a factor of 100 to 1.4ZB (zettabytes).
Remember the frustrations of key word search? If you need a refresher, just check out If Google Was a Guy. It’s funny—in a way that makes you kind of squirm in your seat. Of course, in the early days of search engines, power searchers used Boolean query operators. We have George Boole, 18th-century English mathematician, to thank for that.
In fact, there are still Boolean purists out there who take Google, Bing and other modern search engines to task for no longer fully supporting Boolean queries. Nevertheless, natural language processing (NLP) evolved pretty much to relieve search users from the burden of having to memorize Boolean query operators and how to use them.
Some days, it seems that new machine learningapplications are popping up everywhere you look in the news. In this author’s opinion, the search market seems to have anointed machine learning as the new hotness. This is a fascinating realization, because anyone who has spent more than a couple years deploying search in the enterprise knows that machine learning has been used and applied in exciting and unique ways for years and years.
In my experience, clients tend to conflate what machine learning means when it comes to Enterprise Search. Of course, it’s not their fault – machine learning is everywhere you look! But when Attivio says machine learning, we mean two things:
If you’ve been following the Attivio blog, you’ll know that we have not been shy about sharing our ideas on what we see in the Big Data market right now. But in this post, our CEO, Stephen Baker, shares our predictions for the industry in 2017.
Time will tell how accurate these predictions are, but these are the trends we see bubbling up as we talk to customers, analysts, and others in the ecosystem.
As an Attivio Solutions Architect, I often work to help companies customize (a.k.a., hack) our Cognitive Search and Insight technology to meet their unique search demands. It’s worth sharing some of the most common hacks in the risk space.
Hack #1: “I have tons of unstructured communications in my enterprise — isn’t flagging all variations of a potentially risky situation time consuming and expensive?"
How Enterprise Search and Big Data Find Common Purpose
Forrester’s 2015 Wave on Search and Knowledge-based Discovery is out, offering a fresh perspective on the evolution of search-based applications. We are pleased that Forrester designated Attivio as a Leader in this category and that the evolution described suggests an important connection between next-generation search and the opportunities of Big Data.