Understanding NLP, NLG and NLU

April 12, 2021

In today’s digitally driven world, both enterprises and consumers are looking at better ways to express, communicate, process and achieve their goals. Technologies like Artificial Intelligence have fuelled this growth and lead to many developments that enterprises are leveraging to handle tasks, both easy and complex, for better profitability and user experience.

The notifications sent to your phones, customer service chatbots, virtual assistants and products like Amazon Alexa and Google Home are common examples of AI-driven technologies. It is here that one must have encountered terms like NLP, NLU and NLG. These are methodologies powered by AI principles and often get confused and used interchangeably.

Natural Language Processing, Natural Language Understanding and Natural Language Generation are terms and methods that deal with the understanding, interpreting, and reproducing natural human language either in textual or speech form. They are meant to strengthen and automate the human-machine interaction using AI algorithms to mimic a human-human interaction.

NLU and NLG are subsets of NLP where NLU handles the human to machine aspect of the conversation, NLP the processing and deconstructing and NLG the machine to the human aspect. Refer to the infographic for a better understanding. Let’s understand each one of them in detail.

Natural Language Processing

As human input can be both in the form of text or speech, NLP caters to processing these two human language domains. This processing is deployed using AI and ML algorithms such as neural networks (Recurrent Neural Network), Markov Chains etc. These algorithms convert the textual or vocal data input into an organised or structured format that the computer can easily understand. NLP algorithms start off using computer libraries that have a predefined set of rules or criteria for sorting, segregating, and classification of text or speech. Once the initial process starts,

  • Text is broken down into smaller chunks, and the neural networks come into play for a comparison with previous or existing entries
  • Then named attributes or entities (nouns) are identified
  • Next, the algorithm tries to analyse and find patterns in the text
  • And finally, predict a structured format of data based on the previous steps and formulate answers

Natural Language Understanding

NLU, as mentioned before, handles the human to machine conversation aspect. The computer first needs to understand what the human is saying or typing before it processes it appropriately. NLU works by understanding two things in a conversation: syntax and semantics. Natural human language is full of colloquial, slangs, references, misspellings and ambiguity.

So, the NLU has two tasks to perform:

  • Find out the grammatically correct format and meaning in terms of spellings, punctuations, verbs and tenses etc.
  • And to understand the context and intent both logically and lexically (word meanings and relations) by filtering out ambiguities.

NLU tries to make sense of human input and strives to understand the user’s emotions and feelings. This is known as performing sentiment analysis which helps a machine gain a deeper, unambiguous and humane understanding of the conversation. 

For example, the following user review – “The acting was smooth and the overall movie great!” NLU identifies the verbs such as acting and nouns like a movie and links them to adjectives (context) such as smooth/great.

Natural language Generation

Once the human input has been understood and processed for insights, the machine now needs to revert to the human in the natural language with the correct structure and meaning. NLG facilitates the machine to human conversation and results in answers and responses by converting the NLP’s structured data and patterns into human recognisable speech or text.  NLG makes use of ML algorithms like Markov Chains, RNN, LSTM and Transformers. 

The response needs to be syntactically and semantically correct as well as logically worthwhile for a successful conversation. Unlike NLU and NLP, NLG has clear and structured data to convert into text or speech devoid of uncertainties and anomalies. NLG is a three-step process as follows:

  • Formation of an abstract outlining the content and context
  • Choice of words and expressions to form relevant meaning
  • Formation of structurally and grammatically correct textual output

Conclusion

NLP, NLU and NLG all work to enable and achieve bi-directional conversation between humans and machines. Meaningful bi-directional conversations are helping enterprises automate tasks, restructure and optimise workforce utilisation and increase user satisfaction. This finds application in chatbots, virtual assistants, dashboard analytics report generation and personalised customer recommendations. 

Whether it’s NLP or its subsets, AI and ML form the backbone, requiring huge computing power for quick calculations. E2E Cloud GPUs powered by Nvidia GPU provide low latency, high performance, and accelerated workflows for all AI-related tasks.

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