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What Is NLP? Understanding Natural Language Processing
Learn what natural language processing is, how it works, and why businesses are leaning on this subfield of AI to create new products and better serve customers.
Learn what natural language processing is, how it works, and why businesses are leaning on this subfield of AI to create new products and better serve customers.
You've likely benefited from natural language processing (NLP) without even realizing it. If you've ever changed a flight or gotten styling recommendations while shopping online, you've likely worked with an AI agent powered in part by NLP.
Unless you're a data scientist, NLP can seem mysterious, but it holds the key to how businesses can use AI agents to revolutionize their sales, service, commerce, and marketing capabilities.
In this article, you’ll learn what NLP is, how it works, and what some of its benefits are. You'll also understand common techniques, how companies are using this form of artificial intelligence (AI), and how the field is evolving and influencing the future of business.
Natural language processing is a subfield of AI that uses computer science and linguistics to help computers understand, interpret, and generate language in the same ways as humans. NLP plays an important role in helping computers summarize and analyze text and create human-like responses to power tools like customer support chatbots, AI agents, and AI assistants.
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NLP converts text and audio into logical forms that machines can understand and process. Researchers have spent years breaking down the elements of language — such as the rules it follows and the nuanced differences in meaning — and then creating frameworks so machines can interpret and replicate the same logic.
It starts with collecting and sorting data, analyzing how it works, training the computer on what to do with the information, and then using a variety of models and methods to fine-tune the results to get it closer to human-level interactions.
Here are some of the common elements involved in NLP:
Natural language processing offers important benefits to businesses adopting AI. NLP has created new opportunities to create products and services — such as virtual agents and, more specifically, AI agents — that help companies serve their customers better. For example, the financial industry uses NLP to help manage fraud detection, resolve transaction disputes, and personalize customer engagement.
Here are some ways businesses can use NLP to their advantage:
NLP assists with automating parts of AI-automated workflows that used to required human intervention, reducing repetitive and time consuming tasks and freeing up humans to focus on more strategic work.
Much of the customer data that businesses have is unstructured. The sheer volume of that data would be too much for a human team to classify and organize in a reasonable amount of time, but a computer with NLP capabilities can do it in seconds.
NLP helps computers analyze data and provide actionable insights for developing new products, improving customer service, and identifying market trends.
Search engines and other data retrieval systems use NLP to return more relevant information to users. Rather than humans trying to think of keyword phrases that the computer has used to index information, NLP-powered search engines can assess users' natural language inputs for what they mean and provide the right results.
With NLP, computers can be trained to analyze content and then, given the right prompts, either summarize it for a certain audience in a certain format (such as various social media posts) or use generative AI to write copy (such as for product descriptions).
Natural language processing drives improvements in efficiency by powering tools like Agentforce, the agentic layer of the Salesforce platform. The wide range of AI use cases that use NLP shows the potential for growth in almost every aspect of company operations. Whether it's handling lead qualification for sales teams or deflecting common issues for support teams, natural language processing has demonstrable ROI.
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In natural language processing, computers need human support to understand how to interpret text correctly. As the adage goes, "Garbage in, garbage out." A computer that's given messy, unprocessed text will likely generate poor results.
Here are some of the standardized techniques that can improve a computer's chances when analyzing and producing text.
Which approach your business takes to NLP will depend on what you’re trying to accomplish — there is no one-size-fits-all method. For example, your approach to building a simple chatbot or spam detection tool will be much different than if you want to extract health data from a wide range of sources to, say, accelerate insurance claim processing or identify ideal patients for a pharmaceutical trial.
Let’s explore how these approaches work and some of the most common applications.
In this approach, a human sets rules based on linguistics so the computer doesn’t need to be trained on huge datasets. This method can work well for parsing text for meaning, such as when reviewing customer comments in support tickets. For example, a computer trained with rules-based NLP would count the number of negative or positive terms in a section of text and classify it accordingly.
Text is broken down into smaller segments with the goal of making it easier for the computer to derive meaning and context for the text it's analyzing. It works well for extracting structured information from source material and can be helpful for sentiment analysis. When you're typing a text and auto-complete suggests a word or phrase, that’s statistical NLP at work.
Machine learning uses algorithms to help a computer understand human language. Models are trained on large datasets to spot patterns. Machine learning in natural language processing is often used for summarizing text, sentiment analysis, and translating content into a different language.
Deep learning NLP is a subset of machine learning and is based on human neural networks. This approach relies on very large sets of unstructured data to steadily increase efficiency and accuracy. It can use both text and voice data for its training.
The natural language processing field is booming with its applications spanning a wide range of industries. As a result, NLP’s effect on business operations is becoming increasingly profound. The ability to analyze large amounts of unstructured business data while also helping with manual tasks such as generating meeting notes and call transcripts is just the beginning.
Here are some areas where natural language processing is having a significant impact.
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As powerful as natural language processing and the advantages of AI might be, there are some risks. After all, it involves being trained on large amounts of human data that could lead to issues such as bias, misinterpretation, and tone.
While companies that use NLP might face increasing regulatory and social pressure to address the challenges of AI, the burden for now is on businesses to ensure natural language processing doesn't harm users.
Some companies are taking these ethical challenges very seriously, building in harm detection and data security features. As forward-thinking companies increasingly rely on NLP, they also need to be responsible and accountable.
Companies should be transparent about data sources for generative AI and about how customer information is used and protected. They should also be mindful of inclusivity and lessening the environmental impact of natural language processing. Salesforce has adopted guidelines for responsible AI usage — something that other companies embracing AI should consider.
You can also expect new developments such as real-time translations, greatly simplified user interfaces, and search functions that are increasingly more flexible and better mimic how humans talk. As we've seen in recent years, natural language processing has evolved quickly, and that rate is likely to accelerate.
Now that you have a clearer understanding of what natural language processing entails and its growing effects on businesses and their customers, you're ready to find out how it can work for you in your business. Learn more about Agentforce and how it can create better ways to connect with customers, and help your employees be more productive.