Imagine this. You’re a member of the board at a vocational school. Summer break is approaching, and that can only mean one thing: a new multi-year strategic plan for the coming years. Each program writes its own policy plan, each department submits something to you. The proverbial stack of papers on your digital desk grows by the day, and it's your responsibility to maintain a clear overview. Which policy plans are similar, which departments are in complete disagreement? And what was it again that the different programs thought about sustainability?
Nimeto
“I’m looking for a solution to analyze, compare, and summarize multiple team plans.” With this question, Jelle van Baggem, leader of the Digitalization & IT Team at Nimeto, approached ilionx last summer. Nimeto is a modern vocational school in Utrecht for creative, entrepreneurial students. No surprise, then, that Nimeto keeps up with the times and started looking for a modern solution to analyze texts.
Based on our Text Analytics Service, we got to work and built a great solution allowing Nimeto to analyze its texts. The tool can generate keywords and summaries. In addition, we built a feature to show which documents are similar by measuring how much the topics discussed in each document overlap. Plans addressing the same topics are close to each other, while documents covering completely different subjects are far apart. This way, we can show document similarity in a coordinate system. For example, in the bottom left, we see a cluster of three plans, to which Jelle enthusiastically responded: “Hey, that makes sense! These plans were written by three different departments working together, which is why they reused parts of each other’s texts.”
The analyzed documents in a coordinate system. The documents are anonymized, but the concept is clear: similar documents are positioned close to each other.
Finally, the solution also enables flexible search. If, for example, you search for “sustainability,” it will also find texts about “greening” or “energy saving,” because we taught the computer that these words are strongly related. This makes it easy to find a section of text, even if you don’t remember the exact wording.
I hope you’re just as excited about this as I am, because I think it’s absolutely awesome! It continues to amaze me that we can teach computers to read texts, allowing you to extract so much information at the push of a button. The fact that I was able to develop and implement all these cool techniques for a client makes me proud, and I’ve been enthusiastically jumping into new projects ever since.
The Future of Text Analytics
Since the project at Nimeto, we haven’t been idle. For instance, we launched a project with Mediahuis Noord and Hanze University of Applied Sciences to apply the Text Analytics Service to make journalists’ and communication professionals’ work easier. We also joined a fascinating study by our client Lentis on the differences and similarities between women and men with autism.
Our goal is to make it possible for everyone to use the Text Analytics Service. By integrating our service with SharePoint, we’ll soon make it easy for anyone to generate document summaries, find keywords, and search through documents. Think of board members quickly browsing through business plans, doctors rapidly locating similar cases, or employees retrieving files quickly — they’ll all be able to connect to the Text Analytics tool!
What would you like to extract from your texts?
About the Author
My name is Olav. I’ve been working for a year now in the Text Analytics Team at ilionx, which I combine with my studies in mathematics at the University of Groningen. This unique combination allows me to fully express my passion for math, because let’s face it: how cool is it to calculate with text instead of numbers? If you’re curious about what I or one of my colleagues can do for you, feel free to contact us to get more out of your texts and documents.
Want to know more about the possibilities of Text Analytics?
Contact Peter van Ditmarsch
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