Alyona Medelyan on applications of NLU

This week, I talk with Alyona Medelyan, co-founder and CEO at Thematic and founder and CEO at Entopix. We talk about natural language understanding, the challenges of analyzing unstructured text, and her open source indexing tool Maui that she’s been working on for the past 10 years.

Here are some highlights:

Use cases of Natural Language Understanding

Natural Language Understanding is really a sub area of Natural Language Processing (NLP). In general, NLP deals with using computers to understand human language, but not all NLP tasks require actual understanding. For example, if we take part of speech tagging, when an algorithm decides whether a word is a noun or an adjective or a verb, in order for the the algorithm to perform this accurately, we don’t really need to know what the words mean. You can achieve quite a lot by simply counting how many times part of speech text follow each other, and very simple techniques would be sufficient. On the other hand, if we’re building a dialogue agent, a chat bot like Siri for example, in order to respond meaningfully, Siri would need to understand what each of our statements mean, and this is where the understanding comes in.

Practical applications of NLU for enterprise

A lot of what can be done with NLU is very practical. I’m actually in Portugal at the moment, and I don’t know any Portuguese. Every time I go to a restaurant or buy groceries or search for places, I use Google Translate, so it’s quite practical. In terms of what everyday businesses, not just giants like Google and Apple, can do with NLU, I think the key example would be understanding customer feedback because these days, pretty much everybody has a smart phone. Everybody has written review for a company if they like their services or they didn’t. People will send complaints and so on. With all of this text, businesses become more competitive because they know people can read all these data. Sentiment analysis—one of the techniques that uses natural language understanding to not just understand whether the customer is happy or sad, but also what are the specific things they’re saying the business is good at or which ones they can improve—this can practically help them to compete and get better at their offerings.

Maui: More than a digital librarian

In a traditional library, a librarian categorizes books so that people can find them. In a digital library, Maui takes this role identifying what each book or each document is about. This is what Maui does; its results can be used to improve search and organize documents, but that’s just one of the applications. I also helped companies apply Maui in many interesting ways. One company used it to link advertisers to web pages to display content-relevant ads. Another used it to send users content recommendations. How it differs from Thematic, is Thematic is specially designed to analyze short pieces of text, something that Maui doesn’t do well. Maui works great on written documents where people actually thought about how to write them, and Thematic works better on short text and can detect more fluid themes than Maui.

Our future with chatbots

I think that chatbots and automated personal assistants, even though currently are not particularly well advanced in what they’re doing and require a lot of humans helping, will still become more prevalent in the future. That would mean that we won’t need to interact with people as often. Just like online banking made the cost of making transactions cheaper, customer support will become cheaper, too, thanks to chatbots.

On the other hand, businesses will compete on providing the best deals and the best customer service for their customers. I think they will use more and more natural language understanding to figure out what people say about their business, about the competitors, about the products. In the end, we as customers will be the one who will benefit from all of this.