NLP & NLG Explained: How AI Understands & Generates Language

What is NLP?

Natural language processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. It speaks to the ability of a computer program to understand, interpret, and produce human language in a valuable way. NLP is crucial for enabling machines and AI to read, decipher, and make sense of human language.

Generally speaking, NLP is just the art and the science of understanding natural language speech. But I would say AI ushered in the next generation of NLP, in the sense that LLMs are trained to solve the NLP challenge.

If you were asking, what are LLMs good at? LLMs are good at understanding the semantic meaning of what people are saying, understanding natural language. The actual algorithm of LLMs has nothing to do with that, but it has the mechanism to understand words and languages that are spoken, and has the ability to understand the relationship of what people are looking for and the things that they're looking to accomplish.

Why is NLP important?

NLP is important because it bridges the gap between human language and computer understanding. By enabling machines to comprehend human language, NLP facilitates a wide range of applications, from sentiment analysis to language translation.

This capability is critical for developing technologies that can interact with users in a natural and intuitive manner, enhancing UX and accessibility.

How does NLP work?

At a high level, NLP works by using algorithms to identify and extract the natural language rules, such as syntax and semantics. These algorithms are often based on machine learning (ML) models that are trained on large datasets of human language. The process involves several steps, including tokenization, parsing, and semantic analysis, to transform unstructured dataStructured Data
Structured data is the term used to describe schema markup on websites. With the help of this code, search engines can understand the content of URLs more easily, resulting in enhanced results in the search engine results page known as rich results. Typical examples of this are ratings, events and much more. The Conductor glossary below contains everything you need to know about structured data.
Learn more into a structured format that machines can understand.

NLP is a set of techniques that are used to process and understand the unstructured data of natural language. And there are different techniques to do that, from how you count words to the relationships between words.

For example, a simple technique is tokenization, where you basically assign each word a unique ID, and then you can start tracking how often that ID occurs in different batches of text.

What is NLG (Natural Language Generation)?

Natural language generation (NLG) is a branch of AI that focuses on generating human-like text from data. It is the process of transforming structured data into natural language, enabling machines to communicate information in a way that humans can easily understand. NLG is used to automatically create reports, summaries, and other forms of content very quickly.

NLG is kind of flipping the script on NLP. Natural language processing is like studying the statistics of how likely a word is to appear after another.

For example, if I have a bunch of words, NLP is trying to understand which word is most likely to come next. And it breaks down all the different options of how to complete that sentence.

But then, if you can use that model to predict what word’s going to come next and fill in that word. That's basically what you're trying to do in generation. You're taking those statistics, and creating something with them.

Why is NLG important?

NLG is important because it automates the creation of content, saving time and resources while ensuring consistency and accuracy. It allows businesses to generate personalized content at scale, enhancing customer engagement and satisfaction. NLG is particularly valuable in industries where large volumes of data need to be communicated quickly and effectively.

How does NLG work?

NLG works by using algorithms to convert data into coherent text. The process involves several stages, including content determination, text structuring, and linguistic realization. These stages ensure that the generated text is not only grammatically correct but also contextually relevant and engaging for the target audience.

What's the difference between NLP and NLG?

While both NLP and NLG deal with natural language, they serve different purposes. NLP focuses on understanding and interpreting human language, whereas NLG is concerned with generating human-like text from data.

Basically, NLP is about comprehension, and NLG is about creation.

How are NLP and NLG used?

We’ve covered what NLP and NLG are in theory; now, what do they look like when they’re actually being used?

Chatbots

Chatbots are one of the most common applications of NLP and NLG. They use NLP to understand user queries and NLG to generate appropriate responses. This technology enables businesses to provide 24/7 customer support, improve user engagement, and streamline communication processes.

Search engines

Search engines like Google and Bing leverage NLP to understand and process user queries, delivering relevant search results. NLP helps in interpreting the intent behind search queries, enabling search engines to provide more accurate and contextually relevant results.

AI content generation

AI content generation uses NLG to produce written content automatically. This application is particularly useful for creating product descriptions, reports, and other forms of content that require consistency and scalability. By automating content creation, businesses can focus on strategic tasks and improve operational efficiency.

How are NLP and NLG evolving?

NLP and NLG are continuously evolving, driven by advancements in machine learning and deep learning technologies. The development of sophisticated language models, such as transformer-based models, has significantly improved the accuracy and efficiency of NLP and NLG applications. These advancements are paving the way for more intuitive and human-like interactions between machines and users.

For instance, think about AI agents, virtual assistants that don't just respond to your commands, but can analyze your calendar and emails to anticipate your needs and take actions based on those needs, suggesting meeting reschedules or drafting travel itineraries before you even ask. Those agents are the next frontier of NLP and NLG.

Natural language processing and generation in review

Natural language processing and generation are transforming the way we interact with technology. By enabling machines to understand and produce human language, these technologies are enhancing user experiences and driving innovation across various industries. As NLP and NLG continue to evolve, they will play an increasingly important role in shaping the future of human-computer interaction.

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