1) To Analyze Data:
NLP plays a pivotal role in data analysis, empowering computers to comprehend and interpret data efficiently as humans. For instance, it can read and analyze customer feedback from diverse sources, such as social media, e-mails, and phone calls, providing a comprehensive summary of customer sentiment. Understanding customer sentiment is vital to business success as it offers a holistic view of how customers perceive a company’s products and services.
2) Simplify Data Processing:
NLP’s ability to make computers understand human language simplifies processing vast amounts of data, providing a trustworthy and efficient solution for specific repetitive tasks. Moreover, it significantly minimizes manual workload, allowing businesses to focus on strategic tasks.
In the digital era, customers are active on social media and are engaged in reviewing the company’s products or services, leading to the generation of vast amounts of data. Businesses must address customer issues, if any, to be successful. As a result, they have to scrutinize actively the concerns raised by the customers.
However, it is challenging for businesses to track vast data. Against this backdrop, NLP comes to the rescue, offering various benefits, such as analyzing the data without missing crucial information, identifying sentiment words, and analyzing the overall customer impression while minimizing manual tasks. Businesses can achieve all these tasks quickly with NLP while spending minimal time processing the data as everything is automated.
3) To Retrieve Crucial Information:
NLP aids companies in retrieving crucial information from documents and identifying patterns and trends in data. By analyzing the data, NLP can provide insights and generate a quick, accurate overview of reports.
For example, a company’s annual financial reports contain massive amounts of data, and NLP can summarize the data and offer a quick overview of the report by retrieving crucial information.
NLP Structured Data
NLP helps computers understand human language. However, computers cannot process human language, which is unstructured data. Therefore, it needs to change into structured data.
What is Unstructured Data in NLP?
The language humans use to communicate is unstructured data for a computer, meaning its organization is incompatible with its ability to interpret it quickly.
Hence, it is essential to convert unstructured text into structured text. Here, NLP takes the responsibility of translating unstructured data into structured data. The first step involves converting the unstructured data (human language) into elements.
While the process of converting structured data into unstructured data is known as Natural language understanding (NLU), the reverse, which includes converting structured data to unstructured data, is Natural language generation (NLG). NLU helps the computer find different possible meanings in a sentence. NLG helps to generate human sentences, which can help chatbots communicate with customers like natural customer support agents.
The translation involves dividing each text into elements. From the above example, the shopping cart represents the primary element, with the blue jeans as a sub-element.
For example:
If the user instructs the AI tool, “To add blue jeans to the shopping cart.”
The above sentence needs to change into elements.
The element form is like <shopping cart> and <item>blue jeans</>.
What is Structured Data in NLP?
Structured data is the representation of human language in the form of elements. Thus, the structured data enables computers to understand human language.
Applications of Natural Language Processing:
NLP finds extensive applications across several AI applications and industry sectors.
It is essential for implementing several artificial intelligence (AI) applications. Some applications that extensively employ NLP are spam detection tools, chatbots, and customer satisfaction evaluations.
For example, suppose the user instructs the AI application to add navy blue jeans to their shopping cart while shopping online. Although humans can understand the above sentence, computers cannot. It is unstructured data for the computer.
Translating the unstructured data into a structured format is very important for the computer to understand such instructions. It is one example of where NLP begins effectively, converting human language into a format the computer can comprehend.
Role of NLP:
Otherwise, it helps convert human language into a format the computer can understand.
NLP and Other Technologies
NLP and Computational Linguistics:
Natural language processing uses computational linguistics to help machines understand human conversation. Some AI tools that use computational linguistics include speech recognition software and language translators.
NLP and Machine Learning:
NLP and machine learning enable computers to enhance and perform their efficiency by training them with sample data. Chatbots and sentiment analysis AI tools are some examples of NLP using machine learning.
NLP and Deep Learning:
Deep learning in NLP helps computers to gain knowledge and think accordingly. It acts similarly to human brains. Automatic generation of image captions, automatic text classification, and text translation are some examples of NLP with deep learning.
Applications of Natural Language Processing:
NLP has various advantages and is functional across multiple applications. Some of them include the following,
1) Chatbot:
NLP is responsible for creating chatbots and remote AI assistants that can interact with customers in a natural human language like real customer care agents. These chatbots can perform such tasks by being trained with a large amount of data, enabling them to understand and respond to customer queries efficiently, enabling businesses to automate customer service, and improving response time.
2) Machine Translation:
Several machines accurately translate text and audio from one language to another. NLP ensures such translations. Moreover, such machines can translate even complex sentences without disturbing the meaning of the text, providing a contextually accurate translation.
3) Detect Spam:
Sometimes, there are chances to receive spam e-mails. However, NLP can curb such mail by scanning and identifying them as spam.
These are a few examples of natural language processing among the various applications.
Natural language processing (NLP) is a subbranch of artificial intelligence (AI) that uses deep learning, machine learning, and computational linguistics to develop various applications. It helps computers understand human language, enabling them to perform multiple activities such as text summarization, sentiment analysis, grammar checking, speech recognition, automatic generation of image captions, and many more.
Advantages of Natural Language Processing:
1) To Analyze Data:
NLP plays a pivotal role in data analysis, empowering computers to comprehend and interpret data efficiently as humans. For instance, it can read and analyze customer feedback from diverse sources, such as social media, e-mails, and phone calls, providing a comprehensive summary of customer sentiment. Understanding customer sentiment is vital to business success as it offers a holistic view of how customers perceive a company’s products and services.
2) Simplify Data Processing:
NLP’s ability to make computers understand human language simplifies processing vast amounts of data, providing a trustworthy and efficient solution for specific repetitive tasks. Moreover, it significantly minimizes manual workload, allowing businesses to focus on strategic tasks.
In the digital era, customers are active on social media and are engaged in reviewing the company’s products or services, leading to the generation of vast amounts of data. Businesses must address customer issues, if any, to be successful. As a result, they have to scrutinize actively the concerns raised by the customers.
However, it is challenging for businesses to track vast data. Against this backdrop, NLP comes to the rescue, offering various benefits, such as analyzing the data without missing crucial information, identifying sentiment words, and analyzing the overall customer impression while minimizing manual tasks. Businesses can achieve all these tasks quickly with NLP while spending minimal time processing the data as everything is automated.
3) To Retrieve Crucial Information:
NLP aids companies in retrieving crucial information from documents and identifying patterns and trends in data. By analyzing the data, NLP can provide insights and generate a quick, accurate overview of reports.
For example, a company’s annual financial reports contain massive amounts of data, and NLP can summarize the data and offer a quick overview of the report by retrieving crucial information.
NLP Structured Data
NLP helps computers understand human language. However, computers cannot process human language, which is unstructured data. Therefore, it needs to change into structured data.
What is Unstructured Data in NLP?
The language humans use to communicate is unstructured data for a computer, meaning its organization is incompatible with its ability to interpret it quickly.
Hence, it is essential to convert unstructured text into structured text. Here, NLP takes the responsibility of translating unstructured data into structured data. The first step involves converting the unstructured data (human language) into elements.
While the process of converting structured data into unstructured data is known as Natural language understanding (NLU), the reverse, which includes converting structured data to unstructured data, is Natural language generation (NLG). NLU helps the computer find different possible meanings in a sentence. NLG helps to generate human sentences, which can help chatbots communicate with customers like natural customer support agents.
The translation involves dividing each text into elements. From the above example, the shopping cart represents the primary element, with the blue jeans as a sub-element.
For example:
If the user instructs the AI tool, “To add blue jeans to the shopping cart.”
The above sentence needs to change into elements.
The element form is like <shopping cart> and <item>blue jeans</>.
What is Structured Data in NLP?
Structured data is the representation of human language in the form of elements. Thus, the structured data enables computers to understand human language.
Applications of Natural Language Processing:
NLP finds extensive applications across several AI applications and industry sectors.
It is essential for implementing several artificial intelligence (AI) applications. Some applications that extensively employ NLP are spam detection tools, chatbots, and customer satisfaction evaluations.
For example, suppose the user instructs the AI application to add navy blue jeans to their shopping cart while shopping online. Although humans can understand the above sentence, computers cannot. It is unstructured data for the computer.
Translating the unstructured data into a structured format is very important for the computer to understand such instructions. It is one example of where NLP begins effectively, converting human language into a format the computer can comprehend.
Role of NLP:
Otherwise, it helps convert human language into a format the computer can understand.
NLP and Other Technologies
NLP and Computational Linguistics:
Natural language processing uses computational linguistics to help machines understand human conversation. Some AI tools that use computational linguistics include speech recognition software and language translators.
NLP and Machine Learning:
NLP and machine learning enable computers to enhance and perform their efficiency by training them with sample data. Chatbots and sentiment analysis AI tools are some examples of NLP using machine learning.
NLP and Deep Learning:
Deep learning in NLP helps computers to gain knowledge and think accordingly. It acts similarly to human brains. Automatic generation of image captions, automatic text classification, and text translation are some examples of NLP with deep learning.
Applications of Natural Language Processing:
NLP has various advantages and is functional across multiple applications. Some of them include the following,
1) Chatbot:
NLP is responsible for creating chatbots and remote AI assistants that can interact with customers in a natural human language like real customer care agents. These chatbots can perform such tasks by being trained with a large amount of data, enabling them to understand and respond to customer queries efficiently, enabling businesses to automate customer service, and improving response time.
2) Machine Translation:
Several machines accurately translate text and audio from one language to another. NLP ensures such translations. Moreover, such machines can translate even complex sentences without disturbing the meaning of the text, providing a contextually accurate translation.
3) Detect Spam:
Sometimes, there are chances to receive spam e-mails. However, NLP can curb such mail by scanning and identifying them as spam.
These are a few examples of natural language processing among the various applications.