What is Natural Language Processing (NLP)?


Natural language processing, or NLP, is an artificial intelligence (AI)-based technique that makes machine learning useful for business applications.

According to a 2021 McKinsey survey, more than half of companies use AI for at least one process, and several are in advanced stages of AI implementation.

NLP streamlines the exchange of information between humans and machines so that AI algorithms can receive data in new ways. The technology also has implications for the metaverse as it would allow digital humans inside virtual worlds to become more realistic.

What is NLP?

Natural language processing (NLP) is the interdisciplinary study of linguistics, computer science, and artificial intelligence to build digital systems that can understand human input and respond accordingly.

Essentially, it allows machines that only understand binary languages ​​(0 and 1) to process human languages ​​like English.

NLP has two main subsets, Natural Language Understanding (NLU) and Natural Language Generation (NLG). The first converts human languages ​​into a machine-readable format for AI analysis. Once parsed, NLG generates an appropriate response and sends it back to the human user in the same language.

How does NLP work?

NLP can be applied to both text and speech. For text, it uses Optical Character Recognition (OCR) to convert text in English or any other language into blocks of data that computers can understand.

It takes unstructured text like PDF forms or social media and converts it for automatic processing. In the case of speech, it uses speech recognition techniques to break down audio into linguistic structures called phonemes, or distinct sound units, which are then matched to their text equivalents for machine processing.

Once the text or speech is converted, the NLP engine passes it to an artificial intelligence algorithm, which can use this input to perform various tasks such as solving queries using an FAQ database or generation of a transcript.

After the input data is parsed, it passes through an NLG layer which converts the algorithm’s response into a text or audio format that human users can understand.

Common NLP Tasks in Digital Applications

NLP technology is integrated into software applications and systems to perform a wide variety of tasks. These include:

  • Speech-to-text – Converts voice input to text output to meet use cases such as real-time captioning and meeting transcripts. NLP for text-to-speech is also useful for accessibility purposes.
  • Meaning Disambiguation – An advanced NLP technique that allows machines to understand the contextualized use of words. For example, a chatbot can understand the difference between using “do” in “make the cut” and “make a bet,” through NLP-powered sense disambiguation.
  • Sentiment Analysis – This is one of the most common applications of NLP. It converts human utterances into a machine-readable format to detect specific words and phrases indicating sentiment. NLP used in this way allows social media algorithms to understand which posts are happy, which are sad, etc.
  • Grammar Marking – Here NLP helps to identify the part of speech of a particular word, depending on the context. It is useful for generating accurate meeting transcripts and summaries.
  • Named entity recognition – An NLP engine can recognize and classify text and voice objects. For example, it can identify the word “United Kingdom” as place and “sandwich” as food.
  • These applications are presented in different types of software, including virtual reality (VR) applications.

What does NLP mean in the metaverse?

NLP in the metaverse (or any other virtual environment) would provide VR users with an alternative method of providing input. It would also equip the VR environment with an alternative way to respond to user input.

Typically, VR navigation is done via handheld controllers, gestures, or eye tracking. The user can press a few buttons, move the joystick, scroll up/down, and others, using VR controllers to navigate immersive spaces like the metaverse. NLP adds voice commands to this experience.

For example, a door inside a VR game opens when the player speaks into their microphone. Since the Metaverse tries to replicate real-world experiences with an exceptional degree of realism, voice commands will play a vital role.

Likewise, the digital elements inside the metaverse can also “respond” using NLP. A non-player character (NPC) in a game or a digital human usually responds to virtual reality users using speech bubbles.

NLP would take these interactions to a whole new level, making it possible to generate audio responses complete with linguistic nuance and voice modulation. It could even automatically translate the response into multiple languages ​​to reach a wider audience.

That’s why metaverse companies like Meta Platforms Inc. are launching NLP helpers for developers. In November 2021, Meta launched a voice SDK that allows VR developers to create virtual environments using voice commands and multilingual support.

Why is natural language processing important for XR?

NLP plays a vital role in Extended Reality (XR) because it:

  • Allows users to execute commands even when their hands are busy. This has major implications for field service personnel using XR-assisted technologies.
  • Simplify the experience of browsing and searching the web in virtual reality, offering an alternative to virtual keyboards.
  • Make driving and other hands-free navigation experiences smoother in virtual reality. This is important primarily for gameplay.
  • Makes technologies like the Metaverse more accessible to non-English speaking audiences through automated translation and transcriptions.
  • Powers more realistic virtual assistants, which can process user input in real time. Organizations can use this technology to provide support services in the metaverse.

Keep in mind that NLP is still an evolving technology and its levels of accuracy when processing input are less than 100%.

Although it has great potential for the future, organizations should invest in developing NLP at an experimental stage, train NLP models on varied data, and ensure ethical use of captured voice and text data.

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