Conversational Language Understanding (CLU)

By Ashwin Venugopal -

 





Introduction

One of Azure AI Language's primary custom features is CLU. In order to anticipate general intent and extract crucial information from incoming utterances, CLU assists users in creating unique natural language understanding models. To educate CLU how to reliably predict entities and intentions, the user must tag the data.

Custom Named Entity Recognition

Custom entity recognition takes custom labeled data and extracts specified entities from unstructured text. For example, if you have various contract documents that you want to extract involved parties from, you can train a model to recognize how to predict them.

Custom Text Classification

Custom text classification enables users to classify text or documents as custom defined groups. For example, you can train a model to look at news articles and identify the category they should fall into, such as News or Entertainment.

Question Answering

Question answering is a largely pre-configured function that responds to inputted queries. Documents like FAQs and manuals provide the information needed to respond to these queries.

For example, say you want to make a virtual chat assistant on your company website to answer common questions. You could use a company FAQ as the input document to create the question and answer pairs. Once deployed, your chat assistant can pass input questions to the service, and get the answers as a result.

Conclusion

We have successfully learnt about basics of understanding conversational language. 









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