Summary: Automated Natural Language Processing (AutoNLP) is a technology that enables users to quickly build dozens or hundreds of AI models without worrying about the very manual tasks of training each model and comparing the model to their labeled data.
Automated Natural Language Processing (AutoNLP) is a technology that enables users to quickly build dozens or hundreds of AI models without worrying about the very manual tasks of training each model, comparing the model to their labeled data, and then repeating this process for all modeling approaches to find the best one.
There are numerous modeling approaches in NLP and all of them can provide value. Given a labelled data set, it’s not always clear which modeling approach will be the one with the highest accuracy for that data set.
This problem is further compounded if you are trying to build different models. At Phase, we build hundreds of models to detect different topics, such as negative sentiment towards a company, discussions around diversity issues at work, problems with customers, and so on… Building a good model for each these topic requires testing dozens of different approaches.
Dozens of models; dozens of approaches… This requires hundreds, potentially thousands, of variations of experiments to find the best approaches. This is why we use AutoNLP.
AutoNLP automates the work associated with building models so you can test the many different machine learning architectures and configurations available, and then simply choose the best one.
At Phase, we have pre-built dozens of modeling strategies and we test each of these strategies via our AutoNLP framework.
Our framework standardizes the way models are built so that every single data set can be used within the modeling process. Similarly, we standardize each modeling approach so that we can simply “plug and play” new approaches into the workflow… This means that every single modeling approach can be used to build models with every single data set! Now we can automatically test and explore all the approaches and choose the best one without human intervention.
From a user’s perspective, given a labelled data set (e.g., a list of yes/no examples for a specific text category), our AutoNLP framework builds dozens of models to find the best performing approach. This is what we then use to score your broader pieces of text.
Since this process is automated, we rerun this process on a regular basis as new labeled examples are provided, to generate newer and better models.
There are numerous benefits of using this approach:
These are just some of the reasons why our AutoNLP approach is so helpful in building accurate models quickly. It’s also why Phase NLP can provide so many models to address your own challenges and business needs.