Do you talk to your computer or smartphone? Just a few years ago, that question would have been absurd. But with advances in natural language processing, the likelihood is that you have asked your phone to send a text or search the web for something within the last day.
In fact, natural language processing (NLP) is one aspect of machine learning, big data, and artificial intelligence that has the potential to truly change everything.
In its most basic terms, natural language processing is the ability of a computer to understand natural human speech as it is spoken. It’s the difference between saying, “Siri, where’s the nearest coffee shop?” and, “Search coffee shops ZIP Code 80021.”
For a long time, searches online had to be done by typing in strings of words combined with Boolean search terms that ended up looking and sounding nothing like a conversation. Now, however, you can type a question into Google exactly how you’d ask it to a friend, and Google can reliably provide a good answer.
The same recognition of natural language is being developed for speech. AI assistants like Siri, Cortana, and Google Now are good examples of this.
While it seems simple for a human to answer a natural language question, it’s an incredibly complex task for a computer, requiring many steps computations and predictions, all of which must happen in the cloud and in a split-second.
The fascinating thing is that, while a human inherently understands what is being said, a computer cannot really be said to understand language. It can parse out the different words, the context, the grammatical usage, etc. and then make a prediction about which response will be the best, but it does not actually understand what we are saying.
One goal of NLP is to do away with computer programming languages like Java, Ruby, or C and replace them with natural human instructions and speech. Another ultimate goal is realistic artificial intelligence, wherein the computer can react to and interact with a human flawlessly.
How NLP is Being Used
Computer “assistants” like Siri and Cortana are the most visible use of NLP today, but there are many other applications of NLP in use. As mentioned above, Google has poured a great deal of resources into NLP as it relates to search, allowing us to type or speak a natural question and receive a relevant answer. Google also is using NLP to create predictive text responses to emails in its Inbox email client, allowing users to choose from one of three responses and respond to an email with a single click.
You may have used NLP for yourself if you have ever used the “translate” link inside Facebook to translate a foreign language into your own (with varying results) or used Google translate on Google or Bing search results. A reliable machine translation has been a goal of NLP since the 1950s, and results are improving all the time.
Other programs are being developed and used that can automatically summarize long documents or extract relevant keywords for searching.
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