Natural Language Processing in AI and its Uses
Language and technology have a profound relationship, the understanding of which is necessary for every business. These two are interdependent elements, especially when it comes to using them optimally. But the point at which both converge, you encounter a phenomenon called Natural Language Processing (NLP)!
What is NLP? Simply put, it is the process of breaking down language into a format that both human beings and computers can comprehend. It is the AI method of interacting with an intelligent system through natural languages. You require NLP when you want a smart system, like a robot, to follow your instructions. Let’s look at some uses of NLP.
Accurate translations have perpetually been a crucial concern. It is because translations aren’t merely converting a sentence from one language to another. It involves taking care of many other factors like technical correctness, cultural accuracy, etc. Machine translations accelerate the pace of translation. But in many cases, accuracy has always remained a critical problem, with which companies have often fought.
But AI and NLP make accurate translations possible. You can train algorithms on texts of various languages, thus making it possible to create text with an equivalent meaning in the target language. Together, NLP and AI can support translations in languages that have scripts different from Latin and have unique alphabet structures, characters, etc.
Smart Virtual Assistants
One of the best examples is smart assistants like Alexa, Cortana, and Siri. They leverage the capabilities of NLP to break a language down into word stems, parts of speech, and various other language-related features.
Two vital elements in the linguistic efficiency of virtual assists include natural language understanding (NLU) and natural language generation (NLG). While NLU allows the device to understand the language, the latter enables the machine to speak. That helps generate the response you expect from the device.
NLP-enabled chatbots work in a similar manner as that of smart virtual assistants. They are, in fact, a type of the latter. But the difference here is that instead of voice recognition, chatbots respond to textual input from the user. They are quickly gaining popularity, considering the pace and accuracy of their responses.
An algorithm adequately trained on email textual data can efficiently identify, classify, and tag emails as regular, malicious, or spam. Malicious emails are usually removed from a particular account before the user sees them.
Besides, solid email filters can help reduce the risk of opening malicious emails, thus helping companies combat cyberattacks better.
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