Natural language processing definition

Natural Language Processing: Definition and Key Ideas

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In this post, we will cover a natural language processing definition and see how it differs from other related disciplines such as optical character recognition, voice recognition, and artificial intelligence.

Natural Language Processing: Definition and Key Ideas

Natural language processing (NLP) is a subdiscipline of artificial intelligence that focuses on processing human language.

NLP-based applications perform tasks such as:


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  • Analyzing grammar. Syntactic analysis is one of the key functions of NLP. When analyzing grammar, NLP-based programs will perform tasks such as parsing text, tagging parts of speech, and determining the relationships between sentences.
  • Generating text. Newer language models are capable of generating text from scratch. For instance, GPT-3 is a language model created by OpenAI, and it became famous for its ability to create text that is indistinguishable from that written by humans. There are limitations, however. This language model, for example, cannot think or understand the meaning of the text that it generates.
  • Recognizing speech. Speech recognition and voice recognition are related to NLP, although the meaning of the two terms does differ slightly – and, it should be noted, the definition does differ depending on the source. According to some, voice recognition refers to something akin to voiceprint identification. Speech recognition, on the other hand, refers to a computer’s ability to understand what people say. This capability has given rise to voice user interfaces, voice search, and other voice-enabled products and services.
  • Determining the meaning of text. Semantic analysis refers to the task of assessing the meaning behind words. For instance, the meaning of words can differ depending on the context or the way in which words are used. As mentioned, technically speaking, AI does not understand that meaning. However, it can determine the meaning, which can assist with a variety of other NLP-related tasks.
  • Assessing the emotions behind a text. Sentiment analysis refers to the scoring of a text’s emotional content. That score typically ranges from positive to negative.

NLP is related to adjacent AI disciplines that work with text, such as optical character recognition (OCR), but they technically fall under different fields. OCR, for instance, focuses on recognizing written characters, or patterns, in text, while NLP focuses on text processing. 

Likewise, the definition of voice recognition cited above – the ability to discriminate between individuals’ voices – is more related to recognition than language modeling

That being said, these disciplines are closely related and they are often used in conjunction with one another.

Use Cases of NLP

NLP has driven innovation and it has proven useful in a variety of use cases.

Here are just a few examples of how NLP is used in business:

  • Chatbots. Chatbots are applications that use language to interact with humans via a chat interface. These applications can be used for customer service, technical support, employee training, sales and marketing, and even automation. For example, the WalkMe ActionBot is a chatbot that can be used to both interact with people and automate certain software tasks, such as employee workflows or tasks related to customer service.
  • Brand monitoring. Companies need to stay tuned to their reputation online. Brand monitoring tools allow them to do this by tracking customer sentiment across a number of channels, such as social media. Using sentiment analysis, brand monitoring tools can offer insights into how customers feel about a particular topic, which can include everything from new products to geopolitical events.
  • Voice search. Voice search is being increasingly used in place of text search on mobile devices. Google, Bing, and other search engines have begun integrating voice search not only into search engines but also into other products. Voice search is far more convenient with hands-free devices, and it is also safer when used, for instance, in vehicles.
  • Dictation. Dictation is the transcription of text from voice into documents. One example: automated transcription, through apps such as Otter.ai, which vastly improves productivity, allowing people to focus on more valuable activities.
  • Document processing. Every business must process a large number of documents during its everyday operations. OCR and NLP tools can be used for workplace digitization, to transcribe documents, summarize the text of documents, automate research, automate the intake of new customers, rewrite documents, and more.

These are just a few of the many examples of how NLP and related technologies are being used to add value to the modern business. We are still at the beginning of the AI era, however, which means we are only witnessing the early stages of NLP-based applications. Despite this, businesses are generating significant value from applications such as these.

Why Use NLP?

There are a number of benefits to using NLP-based applications.

A few of these include:

Ultimately, businesses who are able to leverage these types of technology earlier than competitors may also gain a competitive advantage. For example, a search engine that uses semantic search will deliver better results to its users, which can drive increased engagement, which then translates into greater profits.

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