Category: Model Building

How we automatically organize large amounts of text data with topics?

Making sense of volumes of text data in surveys, legal documents, websites, customer support tickets and discussion threads can be daunting. This is why organizations are turning to tags, labels and topics to help organize all of their data. Unfortunately, not all organizations can afford the time to manually create labels for each and every document that they deal with…

How we make sense of emails, social media content and documents with automatic categorization?

Enterprises are overwhelmed with the volume of text they have to deal with every day. You have emails, chats, web pages, social media, support tickets, survey responses, clinical notes, incident reports and a whole lot more that are purely unstructured in nature. While text data can be an extremely rich source of information, manually extracting insights from large volumes of text data is labor intensive…

3 Tips for Building NLP Systems that Scale

A vast majority of NLP solutions developed at the work place just don’t scale! And by scale, we mean handling real world uses cases,  ability to handle large amounts of data and ease of deployment in a production environment. Some of these approaches either work on extremely narrow use cases or have a tough time…
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