Top-6 familiar examples of Natural Language Processing (NLP)
Irrespective of the industry or sector, Natural Language Processing (NLP) is a modern technology that is going deep and wide in the market. Not only in businesses but this innovative technology is typically used in everyday life.
Effective communication is much more than speech or text. It is about the proper context, body language, intent to decipher the precise meaning and much more. And when a machine grasps these nuances and performs accordingly, then it can generate a significant impact on human beings.
But, before going further on how NLP is used in everyday lives, let’s understand the standard definition of NLP.
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What is NLP?
Natural Language Processing or NLP represent a field of Machine Learning which provides a computer with the ability to understand and interpret the human language and process it in the same manner.
In simpler terms, NLP provides a computer with the skills to understand, extract, generate and perform the assigned task accurately.
Now, let’s see how we are using NLP in our lives and its benefits.
Common examples of NLP
1.Spell Check
Spell Check is used by everyone and creates an immense impact on our lives. Everyone has used the spell check feature on a smartphone/laptop/computer. It is a feature that checks for misspellings in a text.
It is extremely tedious and time-consuming to make each sentence grammatically correct and check each spelling. In order to save time, efforts and increase overall productivity, the NLP technology is widely used.
2. Autocomplete feature
Autocomplete represent yet another form of NLP which is being used. It is a feature in which an application automatically completes the remaining sentence which the user wants to type.
It is an effective and extremely convenient method to search or discover precise information. Any grammatical mistake can also be prevented with this feature.
3. Chatbots
A chatbot is also known as conversational agents. It is employed to engross in online conversations with customers/clients without human chat operators.
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Several websites contain a feature of implementing chatbots so that business-related queries and valuable information can be exchanged effectively.
It is also used in online shopping websites. It implements algorithms that embrace NLP technology which helps to understand and respond to the questions automatically, and in real-time.
4. Virtual Assistants
Smart assistance like Siri, Alexa, Google relies completely on Artificial Intelligence (AI) and the superior force behind it is NLP.
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This disruptive AI technology allows machines to properly communicate and accurately perceive the language like humans. Businesses and companies can develop their skills and combine them with their specific products to reap the maximum benefits.
According to a report, around 1.8 billion people are expected to the smart assistant by 2021.
5. Translation
The effective implementation of NLP made the language translation process easier. This is beneficial when trying to communicate with someone in another language. Presently, with the help of google, one can translate various languages.
6. Digital calls
When any service executive responds to a customer query and conveys the required information over a call then these calls are recorded for training purpose.
This is an NLP practice that many companies, including large telecommunications providers, have put to use so that machines can progressively improve and learn from the experiences.
Apart from the aforementioned examples, there are several key areas and sectors where NLP is used extensively. In future, this modern technology will expand when businesses and industries embrace and witness its value.
But to reap the maximum benefit of the technology, one has to feed the algorithms the quality data and training. And when it comes to quality training data, Cogito is a leading marketplace for it. The company offers natural language annotation services for machine learning with the most unparalleled level of accuracy.
We provide possible solutions for wide-ranging needs like speech recognition, sentiment analysis, virtual assistance and chatbots.
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