Bot essentials 6: NLP, NLU & NLG - What do they mean?
Bots use various elements of NLP, NLU and NLG. Think about these terms as a set of libraries with algorithms at the core that help you understand what a user is asking or saying, by breaking down the user input. Analysing it, interpreting it, processing it, understanding its meaning and then searching for the right answer match to the input in a language that the user understands.
NLP: Natural language processing
NLP is an overarching term used for interpreting language elements. It is used when machines are used to break down the input, analyse the structure and construct, interpret the words, understand its meaning, and constructing a response for the user to understand.
NLU: Natural language understanding
A primary subset of NLP but a very important one, which takes unstructured inputs like a segment of a conversation, converts them into objective structure components for machines to understand based on a pre-constructed database of words, associations and constructs.
While humans have the ability to understand or interpret approximately, accents, colloquial references, slang, spelling errors, varied pronunciations and other arbitrary and quirky constructs, machines will have a difficult time doing so without rules and constructs. NLU helps to overcome this varying nature of language.
NLG: Natural language generation
When computers output language, following the rules of sentence construction, word associations, thesaurus use, understanding the form of input to construct appropriate output, NLG converts machine rules and structured association responses into text.
So how do the various components work in conjunction to respond to a user query?
The diagram above explains the architecture of a bot development platform like Engati.
Users use various chat platforms like Facebook messenger and others to launch the bot. They ask queries to the bot or have conversations.
After authentication and verification..
.. the chatbot core engine uses its NLP/NLU engine to interpret and dissect the user query. The matching engine then refers to the database to get the most appropriate response to the query. The response to the query is formulated in the NLP and NLU engine based on the query response.
This manages and enhances the database by various modules in the Admin platform. It includes the training engine to ensure a learning or training mode for the bot. It further helps the bot to grow more intelligent with usage.
- Enbo Speaks
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