What is the Difference Between NLP, NLU, and NLG?

NLP is an already well-established, decades-old field operating at the cross-section of computer science, artificial intelligence, an increasingly data mining. The ultimate of NLP is to read, decipher, understand, and make sense of the human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples of such tasks are online chatbots, text summarizers, auto-generated keyword tabs, as well as tools analyzing the sentiment of a given text. As a result, algorithms search for associations and correlations to infer what the sentence’s most likely meaning is rather than understanding the genuine meaning of human languages.

Natural language processing works by taking unstructured data and converting it into a structured data format. For example, the suffix -ed on a word, like called, indicates past tense, but it has the same nlp nlu base infinitive (to call) as the present tense verb calling. NLP is a branch of artificial intelligence (AI) that bridges human and machine language to enable more natural human-to-computer communication.

Processing Data v/s Understanding Data

Gone are the days when chatbots could only produce programmed and rule-based interactions with their users. Back then, the moment a user strayed from the set format, the chatbot either made the user start over or made the user wait while they find a human to take over the conversation. NLP can process text from grammar, structure, typo, and point of view—but it will be NLU that will help the machine infer the intent behind the language text. So, even though there are many overlaps between NLP and NLU, this differentiation sets them distinctly apart. NLP focuses on processing the text in a literal sense, like what was said. Conversely, NLU focuses on extracting the context and intent, or in other words, what was meant.

Natural language generation is another subset of natural language processing. While natural language understanding focuses on computer reading comprehension, natural language generation enables computers to write. NLG is the process of producing a human language text response based on some data input. This text can also be converted into a speech format through text-to-speech services.

What NLP does

When information goes into a typical NLP system, it goes through various phases, including lexical analysis, discourse integration, pragmatic analysis, parsing, and semantic analysis. It encompasses methods for extracting meaning from text, identifying entities in the text, and extracting information from its structure.NLP enables machines to understand text or speech and generate relevant answers. It is also applied in text classification, document matching, machine translation, named entity recognition, search autocorrect and autocomplete, etc.

It gives machines a form of reasoning or logic, and allows them to infer new facts by deduction. 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. You can learn more about custom NLU components in the developer documentation, and be sure to check out this detailed tutorial. Whether it’s simple chatbots or sophisticated AI assistants, NLP is an integral part of the conversational app building process. And the difference between NLP and NLU is important to remember when building a conversational app because it impacts how well the app interprets what was said and meant by users.

Top 30 NLP Use Cases in 2024: Comprehensive Guide

Here are some of the best NLP papers from the Association for Computational Linguistics 2022 conference. By considering clients’ habits and hobbies, nowadays chatbots recommend holiday packages to customers (see Figure 8). Since it is not a standardized conversation, NLU capabilities are required. The procedure of determining mortgage rates is comparable to that of determining insurance risk. As demonstrated in the video below, mortgage chatbots can also gather, validate, and evaluate data. False patient reviews can hurt both businesses and those seeking treatment.

• Thus, it helps businesses to understand customer needs and offer them personalized products.
• With capabilities such as sentiment analysis, emotion detection, and entity recognition, IBM Watson NLU offers a scalable and accessible solution for businesses looking to integrate advanced text analysis.
• Applications vary from relatively simple tasks like short commands for robots to MT, question-answering, news-gathering, and voice activation.
• Much more complex endeavors might be fully comprehending news articles or shades of meaning within poetry or novels.

Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud, determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. 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. Hugging Face’s Transformers library has gained immense popularity for its collection of pre-trained transformer models. With a vast selection of models, including BERT, GPT-2, and RoBERTa, developers can leverage these powerful tools for tasks such as text summarization, translation, and sentiment analysis.

Natural Language Processing (NLP): 7 Key Techniques

Not only does this save customer support teams hundreds of hours, but it also helps them prioritize urgent tickets. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines. In addition to processing natural language similarly to a human, NLG-trained machines are now able to generate new natural language text—as if written by another human. All this has sparked a lot of interest both from commercial adoption and academics, making NLP one of the most active research topics in AI today.

They may use the wrong words, write fragmented sentences, and misspell or mispronounce words. NLP can analyze text and speech, performing a wide range of tasks that focus primarily on language structure. However, it will not tell you what was meant or intended by specific language. NLU allows computer applications to infer intent from language even when the written or spoken language is flawed. Conversational interfaces are powered primarily by natural language processing (NLP), and a key subset of NLP is natural language understanding (NLU). The terms NLP and NLU are often used interchangeably, but they have slightly different meanings.