Using Natural Language Processing for Artificial Conversation Engines

Using Natural Language Processing for Artificial Conversation Engines

Mindbreeze InSpire's Natural Language Processing Service uses deep learning models to capture the meaning of content and each end user's search query.

In the above video, Trey Norman, COO of Mindbreeze, showcases the different techniques using an Artificial Conversation Engine with Mindbreeze embedded inside the system.

As seen in the video, the bottom right side of the screen opens up the Conversation Engine, and the user can input the query, "What are search applications?" or any other query they may have.

Mindbreeze uses Natural Language Question Answering (NLQA) to both process this query and to provide an answer snippet from inside Mindbreeze documentation. A reference to the full documentation page is also provided straight to the user’s workflow.

Next, the user asks about the preparation required for a Mindbreeze standby appliance. From the content, Mindbreeze understands that this question could be interpreted in multiple ways and presents a list of options to the user. The user selects the option most relevant to their needs, and Mindbreeze once again provides an answer snippet based on its understanding of existing documentation.

The user can always submit feedback regarding if the answer provided was helpful, which is used to enhance the conversation model moving forward.

Finally, the user submits a query asking, "Where can I find a video to install an SSL certificate?"

Through guided navigation, Mindbreeze helps the user find this tutorial video inside the documentation. Once more, a reference is provided to the specific portion of the documentation that contains this video.

So, What Else?

Methods are available for accelerated Natural Language Processing, Natural Language Understanding, and Natural Language Question Answering. These methods are used symmetrically to both analyze content as it is indexed and to analyze user queries.

This enables Mindbreeze to surface insights from human-readable data using human-readable inputs, not keywords, and do this across multiple languages. Natural Language Processing provides users inside your organization with an expert available 24/7 to answer specific questions. It also provides users outside of your organization with an easy avenue for self-support, saving both time and resources of customer support staff.

Want more information on NLQA use cases and how Mindbreeze assists with Artificial Conversation Engines? Contact our team of experts today!