The binary language was invented to bridge the gap between human and machine communication.
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From the time we as humans worked on understanding computer language, we have gradually evolved to a phase where we are currently coming up with breakthrough technologies on making computers understand human language, emotions and sentiments.
This breakthrough tech is called Natural Language Processing or .
As the result of dynamic development, is responsible for most of the innovations we experience in industries and our everyday lives. In fact, some of the applications in real life are so seamless that they have already become an integral part of our lives.
That is also why the market has been consistently becoming inevitable.
Statistics reveal that in 2017, the market was valued at around $3bn. By 2025, experts believe that this market value will increase by 14 times, making it a $43bn industry in the coming years.
A Quick History of
Natural language processing appears futuristic and new but it would be hard to believe that the foundations for the technology were laid in the 1950s. Artificial intelligence is a term coined in the 50s and it was during the same time that Alan Turing also conceived and developed a test for machines that could think.
From the time experts started observing languages as systems, consistent developments and advancements allowed researchers like Naom Chomsky to develop a concept that could translate natural sentences into computer understandable formats in 1957.
After a brief hiatus, emerged again in the 80s with newer ideas and concepts. As tech kept evolving, we gradually developed systems and hardware peripherals that could eliminate all hindrances associated with data generation, storage and processing. With Siri, we witnessed a revolution in and concepts and there has been no turning back ever since.
With
So, here is a detailed post on the uses of
Let’s get started.
10 Real-world examples of Natural processing language
Speech Recognition
Experts believe
Today, we have virtual assistants that can understand our moods, emotions, preferences, modulation and more from the way we talk and come up with human-like responses artificially. Apart from these,
Statistics also reveal that close to 50% of all search queries would be voice-based in the coming months and years.
Semantic Analysis
When humans read, we tend to relate context, situations, sentiments and other abstract concepts to the text and comprehend what is written. Machines, on the other hand, cannot do that. Semantic analysis is the process of making machines understand the semantics associated with a text to comprehend sentences, interpret emotions, analyze grammar and sentence structures and correlate phrases with situations.
With companies and businesses generating massive volumes of unstructured data every day,
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