What is natural language processing (NLP) in AI?

Cost-efficient automation

NLP systems are often used to automate tasks like customer support, email filtering, and document classification. Like any other type of automation, it saves organizations both time and resources.

Data insights

NLP systems can be used by businesses to provide insights or identify trends. By analyzing large volumes of text data retrieved by enterprise chatbots – like customer feedback, reviews, or social media posts – an NLP system can help improve products or services.

Search optimization

Searching has gotten better and better over the years – in part, thanks to NLP. 

NLP enables more accurate search results, whether by voice or text, allowing users to find information faster. We can see these benefits at work every time we type in a Google search, ask Siri to call a cab, or describe the kind of product we want to a store’s AI chatbot. 

Personalization

Since NLP systems analyze individual language patterns and preferences, their responses can be tailored to each individual interaction. 

For example, a customer support chatbot can offer an apology or a discount to a rattled customer, or an AI assistant can suggest a clothing brand that matches its user’s previous preferences.

Natural Language Understanding (NLU)

NLU is necessary for extracting the meaning behind user inputs.

As a subset of NLP, NLU focuses on the comprehension aspect of language processing. Its primary goal is to enable machines to understand and interpret human language in a meaningful way. 

NLU involves analyzing text to determine the intent behind the words, recognize entities, and grasp the contextual meaning of the language. 

For instance, when a user says, "Book a table at the restaurant," NLU is responsible for understanding that the intent is to make a reservation, and "restaurant" is the entity where the action should occur. 

Natural Language Generation (NLG)

NLG, on the other hand, is concerned with the production aspect of language processing. After a machine understands the user's input (thanks to NLU), NLG takes over to generate a coherent and contextually appropriate response. 

For example, a user asks a chatbot, "What's the weather like tomorrow?" The NLG system will formulate a response like, "The weather tomorrow will be sunny with a high of 75°F." 

NLG involves selecting the right words, structuring sentences correctly, and ensuring that the output is natural and human-like. It's a critical component of NLP that turns the machine's understanding into communicative language.

1. Utterances

The utterance is the exact phrase spoken or typed by the user. In this case, it’s: "Schedule a meeting with the marketing team at 3 PM tomorrow."

The utterance is the input that the NLP system will analyze to determine the intent and extract the relevant entities.

2. Entities

The entities in this sentence provide specific details related to the intent. 

For example, one entity here is “marketing team", because it specifies who the meeting is with.  Another entity is "3 PM tomorrow" since it provides the time and date for the meeting.

Entities give the chatbot the necessary information to schedule the meeting correctly.

3. Intents

In our example sentence above, the intent is the user's goal: schedule a meeting. 

A conversational interface, like an AI chatbot, will recognize that the intention behind the user’s message is to set up a meeting.

4. Tokenization

Tokenization is a step of the NLP process. It describes breaking down a sentence into smaller parts, called tokens, which can be individual words, phrases, or even punctuation marks. 

For example, our utterance might be split into tokens like "Schedule," "a," "meeting," "marketing team," "3 PM," and "tomorrow." 

This helps the NLP system analyze each part of the sentence more effectively, making it easier to understand the overall meaning and respond accurately.

5. Stemming and Lemmatization

Stemming and lemmatization are techniques that might be used by NLP systems to simplify words to their base or root form. Stemming cuts a word down to its base – like marking the word ‘scheduling’ as ‘schedule’.

Lemmatization converts words to normalized, existing versions found in a dictionary. So instead of just removing suffixes, lemmatization might mark ‘wowza’ or ‘tight’ as the word ‘good’.

These techniques help the NLP system recognize that words with different endings or forms can have the same meaning.

6. Part-of-Speech Tagging

In this step, the NLP system tags each word in the sentence with its grammatical role:

• Schedule (verb)
• a (determiner)
• meeting (noun)
• with (preposition)
• the (determiner)
• marketing (adjective)
• team (noun)
• at (preposition)
• 3 (number)
• PM (noun)
• tomorrow (noun)

Parts-of-Speech (PoS) tagging helps the NLP system better understand the structure of the sentence and the relationships between the words.

7. Named Entity Recognition (NER)

The system identifies specific entities in the sentence, such as "marketing team" (an organization or group) and "3 PM tomorrow" (a time expression). NER helps the system understand who the meeting is with and when it should be scheduled.

8. Sentiment Analysis

Sentiment analysis assesses the tone of the input. 

If the CMO were to say, "Schedule another meeting with the marketing team before I rip my hair out," the NLP system would recognize the negative sentiment.

Once a sentiment is identified, the NLP system can act accordingly – it may reassure the CMO or apologize. Sentiment analysis is particularly useful when a conversational interface is interacting with customers, as it can measure how many are content and how many are frustrated.

9. Contextual Understanding

NLP systems use contextual understanding to interpret the meaning of words and phrases based on their surrounding text. This involves analyzing not just individual words but how they relate to one another in a sentence or conversation. 

10. Machine Learning 

NLP systems improve their ability to understand and generate language by using a machine learning (ML) model. 

The ML model is trained on a large dataset of sentences, allowing it to correctly interpret the intent ("Schedule a meeting"), identify entities (like "marketing team" and "3 PM tomorrow"), and generate an appropriate response.

11. Dialogue Manager

Dialogue management in NLP systems tracks the context of a conversation, ensuring coherent responses based on previous inputs.

If the CMO mentioned in the morning that he needed to meet with the marketing team, he might say, ‘Schedule that meeting for me at 3pm.’ The system would remember and confirm that he wanted to book it with the marketing team.

Virtual Assistants

It’s probably in your pocket right now: smart assistants like Siri, Alexa and Google Assistant use NLP to understand and respond to voice commands. 

When you ask, "What’s the weather today?" an AI assistant processes your speech, understands the intent, retrieves the weather data, and responds with the relevant information.

AI Chatbots

Many companies use NLP-powered chatbots to handle customer inquiries. For example, if you ask a chatbot on an e-commerce site, "Where is my order?" the bot can interpret your query, access order tracking information, and provide you with an update.

Language Translation

NLP is at the core of translation services, allowing users to translate text or speech from one language to another. 

These systems analyze the structure and meaning of the original language and generate an equivalent text in the target language. That means every time you use Google Translate, you have NLP to thank.

Voice-to-Text Applications

Voice-to-text applications, like Siri or dictation tools, convert spoken language into written text using NLP. 

When you use voice typing on your phone or transcribe a meeting recording, NLP breaks down the sounds into words, recognizes patterns in speech, and produces accurate text. 

Email Spam Filtering

While we don’t think of it as AI-adjacent, email spam filtering is a common application of NLP. 

NLP systems can analyze the content of emails, looking for certain patterns, phrases, or behaviors that indicate spam or phishing – like specific keywords, strange links, or odd formatting.

Text Summarization and Generation

These types of tools condense long articles, reports, or documents into shorter, easy-to-digest summaries – and they do it with NLP. 

And every student in the age of ChatGPT has used a text generator. These NLP generators can often create coherent and meaningful content, from a rhyming stanza, to an English essay, to product descriptions.

How does NLP differ from traditional rule-based language processing?

Traditional rule-based systems depend on manually written linguistic rules, making them rigid and time-consuming to maintain. In contrast, NLP leverages statistical methods and machine learning to learn language patterns from data, allowing for greater adaptability and scalability.

How is NLP different from general machine learning?

NLP is a specialized branch of AI that applies machine learning to understand and generate human language, whereas general machine learning covers a broader range of data types like images, numbers, or signals.

What’s the difference between a domain-specific NLP model and a general-purpose model?

A domain-specific NLP model is trained on text from a particular field, such as law or medicine, allowing it to understand specialized vocabulary and context with greater accuracy. General-purpose models, while more versatile across topics, may lack precision when applied to niche or technical domains.

How can small businesses benefit from NLP technologies?

Small businesses can use NLP to automate customer service with chatbots, improve website search functionality, analyze customer feedback for sentiment, and assist with content creation, helping streamline operations even without a large technical team.

Are there known ethical concerns or biases in NLP models?

Yes, NLP models can unintentionally learn and amplify biases present in their training data, leading to unfair or harmful outputs, which is why responsible training and regular audits are important.