NLP vs NLU vs. NLG: Understanding Chatbot AI

Increasingly, however, RPA is being referred to as IPA, or Intelligent Process Automation, using AI technology to understand and take on increasingly complex tasks. When it comes to relations between these techs, NLU is perceived as an extension of NLP that provides the foundational techniques and methodologies for language processing. NLU builds upon these foundations and performs deep analysis to understand the meaning and intent behind the language.

• NLU allows computer applications to infer intent from language even when the written or spoken language is flawed.
• IBM Watson NLP Library for Embed, powered by Intel processors and optimized with Intel software tools, uses deep learning techniques to extract meaning and meta data from unstructured data.
• NLG techniques provide ideas on how to build symbiotic systems that can take advantage of the knowledge and capabilities of both humans and machines.
• Some of the basic NLP tasks are parsing, stemming, part-of-speech tagging, language detection and identification of semantic relationships.

The task of NLG is to generate natural language from a machine-representation system such as a knowledge base or a logical form. To simplify this, NLG is like a translator that converts data into a “natural language representation”, that a human can understand easily. When given a natural language input, NLU splits that input into individual words — called tokens — which include punctuation and other symbols. The tokens are run through a dictionary that can identify a word and its part of speech.

What is natural language understanding?

Companies can utilize this information to identify trends, detect operational risks, and derive actionable insights. NLG techniques provide ideas on how to build symbiotic systems that can take advantage of the knowledge and capabilities of both humans and machines. When all these models are processed together and facilitated with data in voice or text form, it generates intelligent results, and the software becomes capable of understanding human language. NLP and NLU have made these possible and continue shaping the virtual communication field. Two subsets of artificial intelligence (AI), these technologies enable smart systems to grasp, process, and analyze spoken and written human language to further provide a response and maintain a dialogue.

Both NLU and NLP use supervised learning, which means that they train their models using labelled data. For example, it is the process of recognizing and understanding what people say in social media posts. Where NLP helps machines read and process text and NLU helps them understand text, NLG or Natural Language Generation helps machines write text. When NLP breaks down a sentence, the NLU algorithms come into play to decipher its meaning.

Applications of NLP

Based on some data or query, an NLG system would fill in the blank, like a game of Mad Libs. But over time, natural language generation systems have evolved with the application of hidden Markov chains, recurrent neural networks, and transformers, enabling more dynamic text generation in real time. While each technology has its own unique set of applications and use cases, the lines between them are becoming increasingly blurred as they continue to evolve and converge. With the advancements in machine learning, deep learning, and neural networks, we can expect to see even more powerful and accurate NLP, NLU, and NLG applications in the future. And AI-powered chatbots have become an increasingly popular form of customer service and communication. From answering customer queries to providing support, AI chatbots are solving several problems, and businesses are eager to adopt them.

This text can also be converted into a speech format through text-to-speech services. It uses neural networks and advanced algorithms to learn from large amounts of data, allowing systems to comprehend and interpret language more effectively. NLU often involves incorporating external knowledge sources, such as ontologies, knowledge graphs, or commonsense databases, to enhance understanding. The technology also utilizes semantic role labeling (SRL) to identify the roles and relationships of words or phrases in a sentence with respect to a specific predicate. Summing up, NLP converts unstructured data into a structured format so that the software can understand the given inputs and respond suitably.

Conversations with a meaning

Allowing the chatbot to answer a long compound question we as humans will answer the question. However, when it comes to handling the requests of human customers, it becomes challenging. This is due to the fact that with so many customers from all over the world, there is also a diverse range of languages. At this point, there comes the requirement of something called ‘natural language’ in the world of artificial intelligence. NLP attempts to analyze and understand the text of a given document, and NLU makes it possible to carry out a dialogue with a computer using natural language. Gone are the days when chatbots could only produce programmed and rule-based interactions with their users.

This technology is used in applications like automated report writing, customer service, and content creation. For example, a weather app may use NLG to generate a personalized weather report for a user based on their location and interests. It involves tasks like entity recognition, intent recognition, and context management. ” the chatbot uses NLU to understand that the customer is asking about the business hours of the company and provide a relevant response. NLP, NLU, and NLG are all branches of AI that work together to enable computers to understand and interact with human language.

Why is NLU Important Now?

In this blog article, we have highlighted the difference between NLU and NLP and understand the nuances. Thus informing the user accordingly and handling the utterance per sentence. This quick article will try to give a simple explanation and will help you understand the major difference between them, and give you an understanding of how each is used. In NLU, the texts and speech don’t need to be the same, as NLU can easily understand and confirm the meaning and motive behind each data point and correct them if there is an error. Our open source conversational AI platform includes NLU, and you can customize your pipeline in a modular way to extend the built-in functionality of Rasa’s NLU models.

In other words, NLU is AI that uses computer software to interpret text and any type of unstructured data. NLU can digest a text, translate it into computer language and produce an output in a language that humans can understand. NLU is a subtopic of Natural Language Processing that uses AI to comprehend input made in the form of sentences in text or speech format. It enables computers to understand commands without the formalized syntax of computer languages and it also enables computers to communicate back to humans in their own languages. If a developer wants to build a simple chatbot that produces a series of programmed responses, they could use NLP along with a few machine learning techniques. However, if a developer wants to build an intelligent contextual assistant capable of having sophisticated natural-sounding conversations with users, they would need NLU.

NLP is an umbrella term which encompasses any and everything related to making machines able to process natural language—be it receiving the input, understanding the input, or generating a response. IBM Watson® Natural Language Understanding uses deep learning to extract meaning and metadata from unstructured text data. Get underneath your data using text analytics to extract categories, classification, entities, keywords, sentiment, emotion, relations and syntax. Deploying a rule-based chatbot can only help in handling a portion of the user traffic and answering FAQs.

This allowed LinkedIn to improve its users’ experience and enable them to get more out of their platform. If it is raining outside since cricket is an outdoor game we cannot recommend playing right??? As you can see we need to get it into structured data here so what do we do we make use of intent and entities. Some of the basic NLP tasks are parsing, stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagrammed sentences in primary school then you have done this manually before.

Step 1: Automatic Language Detection

Here the user intention is playing cricket but however, there are many possibilities that should be taken into account. Difference between NLP, NLU, NLG and the possible things which can be achieved when implementing an NLP engine for chatbots. However, the broad ideas that NLP is built upon, and the lack of a formal body to monitor its use, mean that the methods and quality of practice can vary considerably. In any case, clear and impartial evidence to support its effectiveness has yet to emerge. This popularity may have been driven by the fact that practitioners can use it in many different fields and contexts. The theoretical basis for NLP has also attracted criticism for lacking evidence-based support.

NLP is used in industries such finance, e-commerce, and social media, among others. For example, in healthcare, NLP is used to extract medical information from patient records and clinical notes to improve patient care and research. Natural Language Processing is at the core of all conversational AI platforms.

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