Natural Language Processing definition: techniques & examples

Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on computers understanding and interpreting human language like humans do. Simply put, NLP helps computers understand and interact with us through spoken and written language.

NLP works by feeding data to computers to help them understand the meaning, context and nuances of human speech, allowing them to process, analyse and generate text in a way that's useful.

Today, NLP is used in many products and services, like chatbots, virtual assistants and language translation services.

Although humans learn language as babies, there’s a lot of processes involved in teaching computers to do what we do naturally. NLP uses a wide range of methods to understand and analyse human language.

Below are some of the common techniques with detailed explanations and examples 👇

1 - Tokenization

Tokenization involves breaking down a text into smaller units, such as words or sentences, called tokens. This helps to standardise and structure the text for further analysis.

For example, consider the sentence: "The cat sat on the mat."

Tokenization would break this sentence into individual words: "The", "cat", "sat", "on", "the", "mat".

2 - Part-of-speech tagging

Part-of-speech (POS) tagging involves assigning grammatical categories (e.g., noun, verb, adjective) to each word in a sentence. This helps in understanding the syntactic structure of the text.

For example, in the same sentence from above, "The cat sat on the mat."

POS tagging would label "cat" as a noun (NN), "sat" as a verb (VBD) and so on.

3 - Named Entity Recognition (NER)

NER involves identifying and classifying named entities such as persons, organisations, locations, dates and more within a text.

For instance, in the sentence "Apple is headquartered in Cupertino, California,"

NER would recognise "Apple" as an organisation and "Cupertino, California" as a location.

4 - Sentiment analysis

Sentiment analysis aims to determine the sentiment or emotion expressed in a piece of text, whether it's positive, negative or neutral.

This is often used to analyse customer feedback, social media posts and product reviews.

For example, the sentence "I love this product!" would be classified as positive sentiment, while "I hate waiting for customer service" would be classified as negative sentiment.

5 - Text classification

Text classification involves categorising text documents into predefined classes or categories based on their content.

This is commonly used in applications such as spam detection, topic classification and sentiment analysis.

For example, an email spam filter would classify incoming emails as either spam or not spam based on their content.

NLP offers several benefits across various industries and fields.

1 - Enhanced information retrieval

NLP enables the retrieval of relevant information from large volumes of text data.

Techniques such as keyword extraction, document classification and sentiment analysis help users to quickly find, organise and understand information.

This is particularly valuable for search engines, recommendation systems and content management platforms.

2 - Automation of manual tasks

NLP automates various manual tasks that involve processing and analysing text data.

For example, chatbots powered by NLP can handle customer inquiries, provide support and perform transactions without human intervention.

Similarly, document summarisation and text generation tools streamline content creation and report generation, saving time and resources.

3 - Personalised user experience

NLP lets you personalise user experiences by analysing their preferences, behaviour and feedback.

By understanding user intents and sentiments, you can tailor content recommendations and product suggestions to individual preferences, leading to customer satisfaction.

4 - Insights from unstructured data

NLP extracts valuable insights from unstructured text data, such as social media posts, customer reviews and survey responses.

By analysing sentiment and opinions, businesses gain valuable insights into customer preferences and competitor strategies. This helps to inform decision-making processes and marketing campaigns.

5 - Accessibility and inclusivity

For individuals with disabilities or language barriers, NLP improves accessibility.

Text-to-speech and speech-to-text technologies convert spoken language into text and vice versa, making it easier for individuals with visual or hearing impairments to communicate.

NLP faces several limitations that can impact its performance 👇

1 - Ambiguity

Ambiguity refers to words or phrases with multiple meanings. NLP systems may struggle to accurately interpret ambiguous language, leading to misunderstandings and errors.

For example, the word "bank" can refer to a financial institution or the side of a river. This makes it challenging for NLP systems to determine the intended meaning without context.

2 - Contextual understanding

Understanding language requires considering the context in which it is used, including the surrounding words, phrases and cultural nuances.

NLP systems may struggle to accurately interpret language without sufficient context, leading to misinterpretations or inaccuracies.

For example, the meaning of the word "run" in the sentence "I went for a run" differs depending on whether it's interpreted as a noun or a verb, highlighting the importance of context in language comprehension.

3 - Handling negation and irony

Humans make this even harder by the complex ways we use language. For example, any four-year-old understands, “I do not like orange juice”, but for NLP models, but this poses huge challenges.

NLP models may struggle to accurately identify and interpret negations (words like not, never, nor, neither that change the meaning of a sentence) or ironic statements, leading to misinterpretations or incorrect conclusions.

For example, the statement "I'm not unhappy" conveys a positive sentiment despite the presence of the negation "not," highlighting the complexities of language comprehension.

Builder.ai is an app builder that leverages the power of artificial intelligence to create mobile applications.

Our AI, Natasha, the world’s first AI-powered product manager, uses a variety of NLP techniques to do her job. For instance, she uses the Named Entity Recognition technique (discussed below) to pull out interesting facts from a call with the customer, such as which app features are being discussed.

Natasha is capable of understanding natural language in a variety of modalities. For instance, she can process human speech through an audio input and converse with the user via text through a messaging medium.

Furthermore, Natasha can also process large amounts of text stored in a file. When users send their requirements document, Natasha processes the text and summarises the information.

Although she's still a machine, she's able to make wordplay jokes, adopt a particular tone of voice, handle negation and, to a certain extent, irony.

Interact with Natasha by checking out Studio 4.0 👈