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Natural Language Processing (NLP) focuses on "understanding" a given content by extracting information from it. People frequently use NLP with Natural Language Understanding (NLU) and Natural Language Generation (NLG). NLP is an umbrella term where NLG and NLU are subcomponents. In a nutshell, NLG deals with text generation and construction of text based on a given dataset. NLU deals with comprehension and extracting intended meaning based on context. Let’s unwrap them in detail.

What's Natural Language?

Natural Language, also known as Ordinary Language, refers to any language that evolves naturally without external and conscious interventions. It’s the everyday language with humour, sarcasm, emotions and even grammatical errors. Artificial Languages, such as computer programming, or Controlled Languages, such as Simplified Technical English, follow specific rules and do not evolve. They are unambiguous.

What's Natural Language Processing?

Natural Language Processing enables machines to understand human language. It’s the intersection of linguistics, computer science, data science and statistics. NLP extracts key phrases and roots of words to classify them and convert unstructured data to structured data. Morphological segmentation, lemmatization and stemming are frequently-used NLP techniques.

Natural Language Understanding

Natural Language Understanding is a subset of Natural Language Processing that enables machines to understand texts written in natural language. In other words, NLU makes machines read and comprehend what they read. NLU focuses on understanding the meaning of words together rather than processing them separately.

Let’s look at the sentences below:

I want to book a flight.
I want a book for the flight.

Humans, naturally, can differentiate them. However, machines cannot. NLU is the technology that enables them to distinguish and understand meanings.

What's Natural Language Generation?

Natural Language Generation is a subset of Natural Language Processing. NLG enables machines to produce a text response in natural language. While NLU enables computers to understand natural language, NLG makes them respond in natural language.

Let’s have an example to understand the difference between NLU and NLG. When someone asks: “Alexa, what’s the weather in New York?” First, speech-to-text transcribes audio to text. Then NLU processes text and extracts the meaning, and NLG produces an answer. Lastly, text-to-speech converts the text output of NLG to speech to answer back to the user. The combination of speech-to-text and NLU is also known as SLU. The combination of NLP and voice recognition enables several applications, like Alexa.

Leopard and Cheetah are Picovoice’s Speech-to-Text engines that transcribe voice to text, allowing NLU to extract meaning from text. Orca is Picovoice’s text-to-speech engine that reads NLG outputs out loud.

What are the Examples of Natural Language Processing?

NLP is used for various purposes, from generic applications such as language translation and autocorrect to industry-specific applications such as claims processing in the insurance industry and initial resume scanning in recruitment.

  • Named Entity Recognition (NER): Named Entity Recognition is an NLP application that focuses on identifying pre-defined categories such as the names of people, products, enterprises, or expressions of times, quantities, monetary values, and percentages. Media monitoring companies use it to track the PR coverage of enterprises. Map applications use for navigation by detecting addresses.

  • Text Summarization: Summarization captures the most vital information in long texts. Text Summarization can apply to almost anything from meeting minutes to research papers or news articles.

  • Sentiment Analysis: Sentiment Analysis is an application of NLP focused on detecting emotions in a given text. Enterprises use it to track consumer perception by scanning social media posts, call center transcriptions or reviews. When used with Speech Emotion Recognition, they can capture both semantic and vocal emotion.

A Large Language Model can perform several NLP tasks at once. The same model can summarize given content and detect the emotion.

What Makes Natural Language Processing Difficult?

Human communication is not straightforward. We experience it while learning irregularities and idioms in a new language. It’s also hard for native speakers to explain nuances because they’re “natural” for them. New words and rules appear almost every day. One word can mean multiple things and have a hidden meaning. That’s why machines can easily understand constructed languages but not natural language. However, let’s cut them some slack. Processing natural language is challenging. Think about the last time you had a misunderstanding or could not get a joke. Even linguistics experts sometimes have difficulties.