Natural language processing (NLP) has become ubiquitous – it’s used in our email filtering, voice assistants, search engines, text prediction, and AI chatbots.
Businesses use NLP systems to improve their operations and individuals use them daily in their homes.
But while it’s everywhere, NLP is a complex process that few of us consider when we use a language translation service or our phone predicts the next word we’re going to type.
The first step to understanding NLP is defining what it is. Let’s get started!
What is natural language processing?
Natural language processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language.
It enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful.
Why use NLP?
The popularity of NLP has arrived for good reason – for both organizations and individuals. A few of the most common reasons to employ NLP include:
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 – 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 ro 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.
Difference between NLU, NLP, and NLG
NLP is a broad field that encompasses various sub-disciplines, including natural language understanding (NLU) and natural language generation (NLG).
NLP is the overarching domain, while NLU and NLG are specialized areas within it. That’s because natural language processing must involve both understanding, then generation during a back-and-forth conversation.
Natural language understanding (NLP)
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.
11 components of NLP
Natural language processing is a complex processing with several intersecting components.
If you want a better grasp of how NLP works, these are 11 components that illustrate the complexities of the process.
To explain these components, I’ll use an example of a CMO making the following request to an internal chatbot: Please schedule a meeting with the marketing team at 3pm tomorrow.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Real world examples of NLP
If you use technology every day, it’s likely that you engage with NLP systems on a daily basis. These are just a few common examples of how you might interact with natural language processing programs.
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?" the 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.
Deploy an NLP chatbot next month
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