AI has come a long way in understanding natural language in a very human way. However, up until recently, many of these AI possibilities were limited to text written in English, but not anymore!
Before we begin: this article uses some basic ideas and concepts related to the field of Natural Language Processing (NLP) and the BERT model. If you don’t know what these terms mean, we highly recommend reading our non-technical introduction to NLP Making sense of NLP - Part I and What is BERT? articles.
Text data is one of the most common and abundant kinds of data available in companies today, and it often a treasure trove of business opportunities waiting to be tapped.
For example, your customer’s reviews and social media posts are a direct link to their opinions about you, and their emails and support tickets a continuous source of how to improve customer satisfaction. Similarly, your own internal reports are sources of operational risk and opportunities and sources of historical data that can tell powerful stories.
In recent years, AI has made big strides in being able to automatically identify relationships and context in text data, and through that extract critical information locked in it. Or in other words, AI has made it possible to automate the processing of text data and make it a genuine source of quantifiable and actionable insights.
However, as is the nature of many AI advances, many of these capabilities were only available for text written in English. But not anymore.
As of today, you can use the Peltarion Platform and it’s Multilingual BERT implementation to create models that can analyze text data written in any of over 100 languages.
Do you have a lot of text or documents that you or your company need to check manually to find relevant information? Or do you perhaps have a lot of valuable historical information that is locked away in piles of documents?
AI can finally help you process these no matter the language they are written in and the Peltarion Platform makes building a custom AI model a breeze.
If you used the platform before, you might know that we already had a BERT implementation, so maybe you are wondering what’s new. A picture is worth a thousand words, so here are two to help clarify the differences.