media update's Aisling McCarthy looks into what Natural Language Processing (NLP) is, and how it can be used to process data.

NLP is a component of machine learning, which is able to process, sort, and categorise various elements of text. Using the mechanics of language, including sentence construction and word classes, NLP is able to recognise each word according to its word class.

While people would read a sentence as “People generate many documents on a daily basis.”, an NLP system would read the same sentence as "[Noun] [verb] [adjective] [noun] [adverb phrase]". Once each word has been classified, the NLP is able to identify the topics of discussion within a document.

Where is NLP being used?

Virtual assistants, like Siri and Alexa, are the clearest use of NLP today, but the technology has various applications. Google has also invested large amounts into the company’s search function, allowing people to speak or type in natural language and receive a relevant answer.

Facebook and Google’s translation options also utilise NLP, in conjunction with machine learning, in order to improve that function all the time. Other programs are also being developed by various companies to extract keywords from articles, as well as summarise lengthy documents. Social media tracking service amaSocial has implemented NLP technology to quickly and accurately analyse the sentiment of social posts.

NLP can change the way people use technology

The use of NLP has already changed the way people search for things online, no longer having to search “Flower shops ZIP code 1709”. People can now talk to technology in the same way they would speak to their friends, now saying “Hey Siri, where is the nearest flower shop?”

While a human can inherently understand what is being said, a computer cannot really understand language but can work out the words, context, grammatical use, and so forth to predict what response will be best.

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The use of AI-powered technology has seen the dawn of a new age for data processing companies. Read more in our article, How AI makes sense of the media’s Big Data.