Machine Translation
We’ve mentioned it quite a few times now but one major application of NLP is machine translation. The goal of machine translation is to take a text in a source language and create output in a different target language that is both accurate and fluent. Great strides have been made in recent years to improve the quality of machine translation.
This is probably the most familiar example of NLP to most people. Speech-to-text and text-to-speech features are what you see in virtual assistants like Siri and Alexa, which can act on instructions based on what the user says alone.
Summarizing Text
NLP technology has gotten sophisticated enough that uruguay mobile database it is capable of analyzing a long piece of text and creating a summary of it, shortening the text without taking away its core meaning. Older methods would clip and use bits from the original, but later models have become able to create more abstracted summaries of the text.
Sentiment Analysis
This is a technique in NLP used to determine the given “emotion” of particular linguistic data, which can be positive, negative, or neutral. Marketers use this to understand masses of data from consumers regarding how their brand is being perceived and how customers feel about it.