Natural Language Processing

Improve your chatbot by analyzing user messages in 5 steps (Part 2)

This is Part 2 in the post series about improving your chatbot by analyzing user messages. (You can read Part 1 here) Social impact organizations need to pay attention to what users say when communicating with their chatbot. Users often communicate with natural language responses, also known as “free-text” or “open” responses. Organizations may actually …

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Improve your chatbot by analyzing user messages in 5 steps (Part 1)

Interest in and use of chatbots continue to grow among nonprofit organizations as they allow for providing services to and educating constituents at scale in an interactive, engaging way. But once you launch your chatbot and let it interact with users, you will notice that one of its most frequent answers to users is “I’m …

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Training a Python to Explore Holes in Dark Patterns

Data Science students are always looking for new and interesting datasets to train machine learning models. There’s tons of public data out there. Unfortunately, in the US, many of our "public" datasets are difficult to access. The most interesting data is hidden behind dark patterns on corporate and government websites. Here you’ll see how to …

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Which flavor of BERT should you use for your QA task?

Making an intelligent chatbot has never been easier, thanks to the abundance of open source natural language processing libraries, curated datasets and the power of transfer learning. Building a basic question-answering functionality with Transformers library can be as simple as this: Input 1: Load Pretrained Transformer QA Model from transformers import pipeline # Context: a …

Which flavor of BERT should you use for your QA task? Read More »

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