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Introduction to Natural Processing language in AI

In today's digital world, businesses are overpowered with unstructured data. So without the right technology, businesses can't process and analyze a huge volume of unstructured data. So that's whereNatural Processing Language(NLP) comes to the rescue.

NLP is an area of artificial intelligence and computer science. The ultimate goal of Natural Processing language is to focus on helping computers to understand the way that humans speak and write. NLP is the driving force behind machine translation, virtual assistants, sentiment analysis, speech recognition, automatic text summarization, and much more.

A subset technique of Artificial Intelligence which is used to narrow the communication gap between the Computer and Human. Click to explore about our, Future of Natural Language Processing

What is Artificial Intelligence?

Artificial Intelligence refers to machines or systems that mimic the decision-making and problem-solving capabilities of the human mind. AI is an all-around branch of computer science concerned with building intelligent machines capable of performing tasks that require human intelligence. Artificial Intelligence glaring in several forms. A few examples are:

  • Recommendation engines- which provide recommendations for shows based on user habits.
  • Chatbot - Chatbot uses Artificial Intelligence to solve the customer's problem and to provide solutions to that problem.

What is Natural Processing Language?

Natural Processing language is a part of Artificial Intelligence, Human Language, and computer science. It is a technique used to analyze, interpret, manipulate and understand human languages.

NLP allows computers to understand spoken words and text as much as a human can. It combines rule-based modeling of human language with deep learning, statistical, and machine learning models. With these technologies, computers enable us to process human language in the form of voice and text data and understand its meaning.

NLP operates computer programs that translate text from one language to another, rapidly summarize large volumes of texts, and respond to spoken commands in real time. All have interacted with Natural Processing language through digital assistants, voice-operated GPS systems, customer service chatbots, and many others. But it also plays a vital role in growing enterprise solutions that help business operations, simplify critical business processes, and increase team member productivity.

Natural Language Understanding tries to understand the meaning of the given text. Click to explore about our, Natural Language Processing Applications

What are the top 3 NLP Tools?

These are some of the tools used in Artificial Intelligence:

  • Tokenization - It is a common task in NLP. It is a way of separating a piece of text into smaller units called tokens. So here, tokens can be subwords, words, or characters. It helps to create the Bag of Words which helps to create the classification model in Artificial Intelligence.
  • Part of Speech (POS) - POS tagging means assigning one of the parts of speech to the given the word, basically labeling each word of a sentence with its correct part of speech. It helps in performing Sentiment Analysis.
  • Word2vec - It s a group of models that helps drive the relations between words. The process uses the model to map words into to model so that they will be easily understandable by the machine during the creation of the AI model.

Applications of Natural Processing language in AI

The applications of Natural Processing language in Artificial Intelligence

Speech Recognition

Speech Recognition is one of the essential applications of Natural Processing language. for example while talking toGoogle Assistantor any other voice assistant, has one ever wondered how they recognize the voice and how they interpret the meaning of the words, and how it gives an appropriate response? So this is possible because of Speech Recognition.

Speech recognition is a technology that makes a computer capable of converting voice input data to machine-readable format. There are many fields where speech recognition is used, such as translating speech, adding speech-to-text, sending emails, etc.

Auto Correct

Nowadays, much software is available to check the spelling and grammar of the text. This software saves us from embarrassing grammatical and spelling mistakes in our texts, emails, and other documents. So here, NLP plays come into play.

This is one of the most used applications of NLP. This software has many features like giving clarity to the document, correcting grammar, suggesting synonyms, and even predicting the tone of the sentences.

The technologies which are used to narrow down the communication gap between Machine and Human. Click to explore about our, Natural Language Processing in Government

Sentiment Analysis

It is hard for computers to interpret human speech as it also includes sentiments and expressions beyond literal meanings. So with the help of NLU (Natural Language Understanding), a subpart of NLP, the machine can now understand the users' different sentiments.

So through sentiment analysis, one can easily handle social media disputes and analyze customer reactions.

Voice Assistants and Chatbots

Voice assistant is software that uses speech recognition and Natural Processing language to understand a user's voice and performs accordingly.

In the same way, chatbots are programs designed to respond to and assist a user 24/7 and answer all the user's queries.

Social Media Analytics

Social media is integral to everyone's life; many people post their thoughts about products and businesses. So with the help of posts posted by people, companies can understand their market position and collect customer reviews by analyzing those data.

But due to a large amount of data, it is tough to grasp the information. So this is where NLP and sentiment analysis comes into play. The structure and identifying the data the users provide help businesses improve.

NLP solutions to facilitate more useful results from your text, Empower Intelligent Search to produce outcomes, and facilitate Sentiment Analysis. Click to explore about our, AWS Natural Language Processing

What are the Challenges of Natural Processing Language?

The significant difficulties come from the amount of data and also data complexity. As NLP is still a new technology, so there are a lot of unsolved problems that come with deploying Natural Processing language systems. So here are some challenges of NLP.

  • Development Time - An AI must calculate many data points to be appropriately trained. So, processing all the data might take a lifetime if someone uses a normal unpowered PC. With the help of many GPUs and deep networks working together, that training period may be reduced. However, if someone is using pre-existing NLP technologies, one will first need to figure time to build the product.
  • Misspellings - For humans, it is easy to challenge to solve Misspellings; humans can quickly detect a misspelled word with it correctly spelled and can understand the remainder of the phrase. But on the other hand, it is difficult for machines to detect.
  • Phrases with Multiple Intentions - This is one of the significant challenges because specific queries and words have many meanings, so the NLP system won't be able to simplify the issue by simply understanding one of them. Suppose, for example, if someone had written on the chatbot that "I have to cancel my prior order and change my card on file," AI must not be able to differentiate between these intentions.
A subset of Artificial Intelligence increasing its necessity with the improvement of its sub-technologies day by day. Click to explore about our, Applications of NLP For Businesses

Conclusion

Natural Language Processing is the part of AI that studies how machines interact with human language. Daily by daily, NLP is working behind the stage to enhance the tools used by everyone, like language translators, chatbots, spell-checker, and many more. Combined with ML algorithms and AI NLP, learn to perform tasks better through experience.

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