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You're reading from  Conversational AI with Rasa

Product typeBook
Published inOct 2021
PublisherPackt
ISBN-139781801077057
Edition1st Edition
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Authors (2):
Xiaoquan Kong
Xiaoquan Kong
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Xiaoquan Kong

Xiaoquan is a machine learning expert specializing in NLP applications. He has extensive experience in leading teams to build NLP platforms in several Fortune Global 500 companies. He is a Google developer expert in Machine Learning and has been actively involved in contributions to TensorFlow for many years. He also has actively contributed to the development of the Rasa framework since the early stage and became a Rasa Superhero in 2018. He manages the Rasa Chinese community and has also participated in the Chinese localization of TensorFlow documents as a technical reviewer.
Read more about Xiaoquan Kong

Guan Wang
Guan Wang
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Guan Wang

Guan is currently working on Al applications and research for the insurance industry. Prior to that, he was a machine learning researcher at several industry Al labs. He was raised and educated in Mainland China, lived in Hong Kong for 10 years before relocating to Singapore in 2020. Guan holds BSc degrees in Physics and Computer Science from Peking University, and an MPhil degree in Physics from HKUST. Guan is an active tech blogger and community contributor to open source projects including Rasa, receiving more than10,000 stars for his own projects on Github.
Read more about Guan Wang

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Using entity roles to distinguish semantics roles in entities of the same type

Rasa offers entity roles in which to distinguish the different roles of the same entity type. Let's take a look at an example of booking a flight ticket between New York and Chicago. If the system does not distinguish the departure and destination roles of the city entity, the bot will not able to understand whether the ticket is from New York to Chicago or from Chicago to New York. With the entity role, the entity has additional information that represents its semantic role (such as the departure or the destination), which will solve this problem for the bot.

To use entity roles, we need to annotate our training data with the role information along with the entity type. Some sample training data appears as follows:

A flight ticket from [New York]{"entity": "city", "role": "departure"} to [Chicago]{"entity":"city", "role":"...
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Conversational AI with Rasa
Published in: Oct 2021Publisher: PacktISBN-13: 9781801077057

Authors (2)

author image
Xiaoquan Kong

Xiaoquan is a machine learning expert specializing in NLP applications. He has extensive experience in leading teams to build NLP platforms in several Fortune Global 500 companies. He is a Google developer expert in Machine Learning and has been actively involved in contributions to TensorFlow for many years. He also has actively contributed to the development of the Rasa framework since the early stage and became a Rasa Superhero in 2018. He manages the Rasa Chinese community and has also participated in the Chinese localization of TensorFlow documents as a technical reviewer.
Read more about Xiaoquan Kong

author image
Guan Wang

Guan is currently working on Al applications and research for the insurance industry. Prior to that, he was a machine learning researcher at several industry Al labs. He was raised and educated in Mainland China, lived in Hong Kong for 10 years before relocating to Singapore in 2020. Guan holds BSc degrees in Physics and Computer Science from Peking University, and an MPhil degree in Physics from HKUST. Guan is an active tech blogger and community contributor to open source projects including Rasa, receiving more than10,000 stars for his own projects on Github.
Read more about Guan Wang