Natural language processing (NLP) is ubiquitous and crucial to our personal and professional success. We casually issue voice instructions to our virtual house assistants, smartphones, and without much consideration, cars.
Natural Language Processing and Machine Learning (ML) have enabled voice-enabled apps like Alexa and Siri to provide information, add events to our daily schedules, and make phone calls to the numbers we specify.
The progress in natural language processing is not only improving our daily lives and activities but also radically altering the ways in which we do business, socialize with friends, and have fun, courtesy of Natural Language Processing.
The Distinction between Machine Learning, Artificial Intelligence (AI), and Natural Language Processing
There are distinct differences between Natural Language Processing, Machine Learning, and Artificial Intelligence, despite the fact that these terms are often used interchangeably. Natural language processing (NLP) and machine learning (ML) are two branches of artificial intelligence (AI) that aim to mimic human intellect.
Artificial Intelligence (AI)
It is a subfield of computer science that allows machines to accomplish tasks normally performed by living organisms. Modern society can benefit greatly from AI’s various features. Natural language processing and machine learning are two branches of artificial intelligence.
Natural Language Processing (NLP)
NLP is a type of artificial intelligence that allows computers to comprehend speech on a human level. Speech recognition, sentiment analysis, and automatic text summarization are just some of the tasks that machines may accomplish with the help of natural language processing (NLP).
Machine Learning (ML)
It is a branch of artificial intelligence that allows machines to improve themselves through encounters without being given any instructions. Automating procedures and providing precise responses are only two ways in which Machine Learning may aid with AI problem-solving and boost natural language processing.
Application of Natural Language Processing (NLP)
Natural language processing is used in a wide variety of applications, including translation, recognition of speech, analyzing sentiments, question and answer systems, chatbots, automatic text summarization, market intelligence, automatic text classification, and automatic grammar checking. Technology like this aids businesses in many ways, including data analysis, insight discovery, the automation of tedious tasks, and competitive advantage.
Translating from one language to another involves more than just switching words around. Translating a text is difficult because different languages have their own set of grammar rules, making it difficult to preserve the original meaning and style. Computers cannot grasp syntax, so they require a method through which a phrase can be dissected and rebuilt in another language.
If you need to translate text online, chances are good that you’ll use Google Translate. Once upon a time, Google Translate relied on Phrase-Based Machine Translation (PBMT), which compared phrases from different languages. Google currently employs Google Neural Machine Translation (GNMT), which combines ML and NLP to analyze linguistic patterns.
We call “speech recognition,” the process through which a computer can decipher human speech and turn it into a format that a computer can understand. It employs natural language processing to let computers act like people and machine learning to make responses that sound human.
Some of the most well-known instances of speech recognition are Google Now, Alexa, and Siri. A mobile device can be instructed to call the person designated as “Mum” simply by saying, “call Mum.”
Sentiment analysis is a subfield of machine learning and natural language processing that deciphers the tone of unreliable sources like news stories and social media posts to conclude the author’s intended meaning. Customer sentiment for a brand, product, or service can be determined by classifying comments into positive, negative, or neutral categories. Public opinion polls, brand reputation tracking, and consumer experience research are all possible thanks to sentiment analysis.
When it comes to the stock market, human emotions can have a significant impact. Stock prices can fall when investors are pessimistic about a company’s prospects, while a positive mood might encourage investors to buy more of the stock, leading to higher prices.
Benefits of Natural Language Processing
Once natural language processing is set up and running, it saves money and time compared to hiring an employee.
Using natural language processing, firms can improve their response times for customer support inquiries. Clients can always expect prompt responses to their inquiries any time of day or night, seven days a week.
Raise contentment among current clients: Natural language processing methods can automatically assess and filter customer care issues by topic, intent, urgency, and sentiment to ensure that no customer is ever left in the lurch.
To better comprehend your market: The use of NLP has a profound effect on advertising. Using natural language processing (NLP) to comprehend your clientele’s terminology will help you better segment your market, personalize your marketing efforts, and reduce customer attrition.
Disadvantages of Natural Language Processing
It takes time to train. It can take weeks to get good results from a new model if it is not built on top of a pre-trained model.
One of the natural language processing flaws is that machine learning (ML) is not foolproof. Predictions and results should be interpreted with the understanding that mistakes can and will be made.
Problems telling apart related phrases. The difficulty distinguishing between words that sound alike is perhaps NLP’s biggest drawback. Identical-sounding but conceptually distinct words are what we mean when we talk about “similar expressions.” Such expressions include “I scream and ice cream,”
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