What Is NLP? Understanding Natural Language Processing

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.

What we'll cover:

• What is NLP (natural language processing)?
• How NLP works
• Benefits of NLP
• NLP techniques
• Approaches to NLP
• How businesses are using NLP today
• The future of NLP
• Optimize your business with AI agents

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.

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:

• Text input/data collection: NLP begins with data. This data can come from almost any text-based source including structured data (like information pulled from a customer relationship management (CRM) system. It can also come from unstructured data such as books, news articles, company information, social media feeds, etc.
• Feature extraction: In this stage, elements of the raw data are assigned numerical values in a matrix. This makes it easier for a computer to understand text while reducing the computational power needed for processing. One way to think about feature extraction is that it removes data that's less important for meaning (i.e., punctuation, stop words like "the," etc.) and sorts everything else into categories.
• Text analysis: This part of NLP helps derive meaning and insights from large amounts of unstructured data. For example, the source of data could be social media reviews of your business or the contents of support tickets handled by your customer service team.
• Computational linguistics: This branch of science involves studying language to help computers better understand human speech. In NLP, computational linguistics advances the semantics and sentiment analysis of language so computers can recognize words, phrases, and idioms that require more than simple logic to understand.
• Model training: Giving computers examples of language used correctly helps them understand context and meaning so they can make predictions, such as the most likely next word in a sentence. Models can be large language models (LLMs), statistical, or complex neural networks. Small language models are typically used for tasks where you need to prioritize scanning targeted sets of information quickly and cost-efficiently, such as customer sentiment and complaint analysis.
• Machine learning: Natural language processing relies heavily on machine learning (ML), where a computer creates outputs based on the data it's fed and then tests the success of the outputs. This can be supervised or unsupervised. In supervised machine learning, a computer analyzes data labeled by humans. In unsupervised machine learning, the computer parses unlabeled data to find patterns. Reinforcement learning is often used to improve accuracy through reward-based trial and error.
• Deep learning: A form of machine learning, deep learning mimics the human brain's structure and uses computational power to find patterns in data. This requires larger amounts of data and more time to process.

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.

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.

• Text pre-processing: This is the overarching term used to describe the process of cleaning up text for machine learning. A data scientist might use one or more of the following methods to ensure the computer has the best chance to understand the text.
• Stop word removal: In this process, articles ("a," "an," "the"), prepositions ("in," "of," "on,"), and other words that have little effect on meaning are removed to improve language processing. Removal of those words can happen before the text is processed or afterward.
• Tokenization: This step takes sentences and breaks them down into individual words so a computer can understand the data.
• Lemmatizing and stemming: Lemmatizing reduces words to their root form so the model's input can be simplified and standardized. For example, words such as "dancing" and "danced" can be narrowed down to the root "dance." Stemming is a quicker method of reducing words to their base, but it trades accuracy for speed since it can sometimes introduce made-up terms.
• Part of speech tagging: This is essentially diagramming sentences and categorizing words in their naturally used format as nouns, verbs, and so on.
• Text classification: This process assigns classes or categories to text, helping a computer understand the context for a particular piece of data. For example, knowing whether the text is from an online restaurant review, a news article, or spam helps the computer know the correct sentiment.
• Disambiguation: The English language in particular is notorious for words that are spelled a single way but have mutiple meanings. Take the word "draw," which can be used to describe the creation of a picture or to pull something out. Disambiguation, employed via machine learning or other techniques, helps clarify context and meaning.
• Sentiment analysis: Classes and categories supplied by humans can help a computer understand whether an online review is positive, negative, or neutral. This approach is especially helpful for AI agents. By detecting sentiments like anger or frustration, they can quickly escalate a customer's issue to a human representative.
• Speech-to-text: Computers can be trained on audio models to help them handle speech-to-text. This technique helps with generating accurate transcripts from meetings, for example.
• Translating text: This helps a computer identify a text's language and then translate it into another language. If you've gone to a website written in a language you can't understand, chances are your web browser has offered to translate the page for you via this method.
• Text generation: Algorithms and language models help AI create content. When done effectively, AI-generated text is accurate, informative, and grammatically correct. For example, a person could prompt an AI tool to tell a joke, write a short children's story, or draft a haiku.
• Conversational agents: Unlike traditional chatbots, which interact with humans via a rules-based hierarchy, conversational AI agents are different because they can be trained with NLP to conduct human-like conversations.

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.

• Sales: Natural language processing makes it easier to draft emails, mimic human reasoning, analyze and learn from sales calls, and measure performance. Conversational AI tools can also help sales reps better use their most essential piece of software, their CRM. For example, when you’re on a call where a potential customer is asking for data, you can type a natural-language question in Slack to your AI agent to pull the info from your CRM without pausing your conversation. Sales organizations can create highly effective AI agents using NLP trained on customer data.
• Customer service: NLP powers personalized service interactions via AI chatbots, and autonomous agents, leading to reduced response times and higher deflection rates. It also makes self-service far easier and more effective.
• Marketing: It helps marketing teams target the right customers, gauge sentiment, and draft content for engagement. For example, Agentforce can help marketers design campaigns and write briefs as well as produce an audience segment analysis thanks to powerful NLP.
• Commerce: NLP helps companies improve their commerce platforms by understanding your target audience’s product needs, tracking trends and feedback, and analyzing user behavior that exists in the review sections on your site or out on social media. AI agents for manufacturers and retailers can use NLP to absorb vast catalogs of information about your business to write product descriptions, understand customers’ posted reviews, and optimize any text related to your product or service.
• Industry specialization: This technology helps facilitate advancements in industries as varied as healthcare, government, media, nonprofits, and more. From greater team collaboration to faster case resolutions, NLP is being used to create greater efficiencies and better user outcomes.

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.