The binary language was invented to bridge the gap between human and machine communication.
Copyright by www.techshout.com
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 NLP.
As the result of dynamic artificial intelligence development, NLP is responsible for most of the innovations we experience in industries and our everyday lives. In fact, some of the NLP applications in real life are so seamless that they have already become an integral part of our lives.
That is also why the NLP market has been consistently becoming inevitable.
Statistics reveal that in 2017, the NLP 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 NLP
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.
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After a brief hiatus, NLP 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 NLP and machine learning concepts and there has been no turning back ever since.
With NLP gaining grounds, more light had to be shed on the innovations and applications made possible by the technology.
So, here is a detailed post on the uses of natural language processing handpicked and curated for understanding.
Let’s get started.
10 Real-world examples of Natural processing language
Speech Recognition
Experts believe speech recognition is the future and the dynamics of human-machine interfaces would completely change with more integration of voice recognition. This futuristic vision could be driven only by a technology like NLP that converts human language into a format that is understandable by machines.
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, NLP is also used by businesses to transcribe calls, recordings, voice notes and other audio-based data and even translate them into other languages.
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, NLP with its semantic analysis application can help organizations make sense of text-based data for business decisions and insights. This could be as simple as understanding a customer email to understanding sarcasm in social media reviews. […]
Read more: www.techshout.com
The binary language was invented to bridge the gap between human and machine communication.
Copyright by www.techshout.com
This breakthrough tech is called Natural Language Processing or NLP.
As the result of dynamic artificial intelligence development, NLP is responsible for most of the innovations we experience in industries and our everyday lives. In fact, some of the NLP applications in real life are so seamless that they have already become an integral part of our lives.
That is also why the NLP market has been consistently becoming inevitable.
Statistics reveal that in 2017, the NLP 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 NLP
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.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
After a brief hiatus, NLP 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 NLP and machine learning concepts and there has been no turning back ever since.
With NLP gaining grounds, more light had to be shed on the innovations and applications made possible by the technology.
So, here is a detailed post on the uses of natural language processing handpicked and curated for understanding.
Let’s get started.
10 Real-world examples of Natural processing language
Speech Recognition
Experts believe speech recognition is the future and the dynamics of human-machine interfaces would completely change with more integration of voice recognition. This futuristic vision could be driven only by a technology like NLP that converts human language into a format that is understandable by machines.
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, NLP is also used by businesses to transcribe calls, recordings, voice notes and other audio-based data and even translate them into other languages.
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, NLP with its semantic analysis application can help organizations make sense of text-based data for business decisions and insights. This could be as simple as understanding a customer email to understanding sarcasm in social media reviews. […]
Read more: www.techshout.com
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