Modern chatbots support multiple languages and voice depending upon the requirements of an application.
So how does computer undertands what people say ?
The computer understands what people say through the process of segmentation. The program extracts meaningful data i.e. entities from the sentences and operates into it. This is referred to as by Named Entity Recognition (NER) in NLP.
NER is a process of locating words in a sentence with the predefined categories such as names, countries, city, service, number, etc. These can be general-purpose like geo.cities or can be custom as defined by program requirements. eg: Ticket type can hold anything like the express, economy, or general.
Eg: What is the area of Nepal?
So here area is one of the entity[Named Entity], the other related entity in the same category can be population, mortality, and many more.
In the above example, Nepal is also another entity that our NLP program will intercept. This can be listed in geo.country.
So based on this the chatbot responds and gives relevant information from the information graph available.
Some more examples:
- Book Plan ticket for two?
- Available shows after 2 pm today?
- Do you have a table available for reservation after 8 pm today?
Luckily for us, lots of Conversation Bot platforms from giant companies like IBM Watson, Microsoft, Google have trained millions of datasets over years and have helped to understand and synthesis what users say using a state of art NLP[Natural Language Processing]algorithms. So can do this sentence segmentation and Named Entity Recognization with a high level of accuracy.
This has eased modern chatbots to understand different variations of the same sentence a real human practice.
Customizing chatbots with webhooks.
Before going on to this section I would like to give some insights to my fellow readers on how modern chatbots work. The commercial chatbots for banking, insurance, education, tourism marketing, sales, and automation are curated with custom services as per the requirements. This includes designing custom services with webhooks.
After the user text is segmented by the state of the art NLP algorithms and then it is connected with the databases for further data operation and manipulations and the desired response is returned back to the user. The response depends on the type of messaging platform and hardware used. which varies in from text video and audio. The choice of program language can be anything a programmer wishes to.
If you like to learn more about this COVID-19 chatbot, implement custom features and work on this project email me @ firstname.lastname@example.org