The AI world is all about Natural Language Processing (NLP). Market is going to burst because there has never been a technology which can understand and comprehend human language better. NLP is one of the best components of Artificial Intelligence. And is one of the prime reasons most businesses are shifting towards Artificial Intelligence development and its possible use cases.
Natural Language Processing allows computers to understand human language. It is the main technology behind chatbots, language translations, speech recognition, spam filters, grammar checks, and so on. As businesses move toward more streamlined processes, the applications of NLP should become more evident.
Have you ever thought that digital assistants like Siri and Alexa work? How are they able to understand what the user is saying? Well, a bit of this is possible through “Natural Language Processing”. One of the latest examples of NLP applications is ChatGPT, which is a popular chatbot launched by OpenAI in 2022. It is very popular among ai application development company. And both ChatGPT and Google Bard use NLP to provide responses. But how exactly does Natural Language Processing work?
What is Natural Language Processing?
Natural Language Processing is a branch of Machine Learning which is a component of Artificial Intelligence, which allows computers to learn, understand, and comprehend human language. Thus NLP combines computational linguistics, that is rule-based modelling of human language with statistical machine learning and deep learning models. These technologies work in combination to allow computers to understand human language in the form of text or speech, and comprehend complete information.
Earlier humans communicated with the machines through programming languages which were coded through a specific command. Code is structured with logic and reasoning, so the same command will give rise to the same output.
However, human language is different from this. It is unstructured and much more complex than programming languages. This is to say, the same word can have several meanings depending on its context and intonation. Besides, there are several languages.
So, this is where NLP comes into place. Natural Language Processing is how computer programs can translate text from one language to another, provide responses to the spoken language, give responses to spoken commands, and write summaries for huge amounts of text. Thus there is a high possibility you have already used NLP in your day-to-day life in the form of voice assistants, chatbots at customer service, GPS systems, and others.
But how does it all work? How is NLP able to translate the human language for machines? Let’s find out.
How Does Natural Language Processing Work?
In simple words, machine learning is used for the training of NLP. Machine learning is a component that utilizes large amounts of data in an algorithm, and uses it to come up with precise data and information. The more data the algorithm is trained on, the better it works.
Computers and machines are continuously evolving and improving and NLP is the technology behind it. Natural Language Processing works by preprocessing the text and running it through the ML-trained algorithm.
Here are the techniques that are involved in the working of NLP:
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Machine Learning
Machine learning is a component that is used for the training of computers through given data to enhance the efficiency of the algorithm. Human languages have different parts of speech, grammar, and contexts. The programmers use Machine Learning to train NLP about human languages and train it to understand the context of it.
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Deep Learning
Deep learning is a subset of Machine Learning which allows computers to understand, think, and learn like humans. It consists of a neural network which contains data processing nodes with a framework almost like the human brain. Deep learning is used by computers to understand complex tasks and provide input for it.
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Computational Linguistics
Computational linguistics is the science of learning and creating human language models using computers and software tools. The programmers use computational linguistics like syntactic, and semantic analysis, to build frameworks that allow computers to understand human conversations.
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Pre-Processing
The NLP uses pre-processing techniques to understand human language: Here are the steps involved in pre-processing:
Tokenization: Tokenization is the process of dividing text into smaller units (tokens). Thus the text is divided into small words or clauses. Tokenization is important because it breaks the text and helps the computer to understand the words that are there.
Stemming: Stemming is the process that allows it to get rid of any affixes in a word. Affixes are added to the start and end of the word which gives it a bit different meaning.
Lemmatization: It is complex but much more precise than stemming. It includes reducing the word to a ‘lemma’ which is like the base form of a word. This technique considers the context of the word and is more accurate.
Stop word removal: Another quite efficient technique is removing stop words. It simply involves removing words that account for no meaning to the sentence.
What are Natural Language Processing Tasks?
As soon as the text is pre-processed the NLP task breaks down the human text and voice data in ways to let the computer understand the intent. Here are the steps involved in NLP tasks:
- Sentiment Analysis: This involves understanding and categorising the sentiment of the text. For example, whether a product review is positive, negative, or neutral.
- Speech Recognition: It is the process of converting voice data into textual information. It is important for speech recognition to understand the accent, intonation, and related tasks.
- Topic Classification: This is where the main topic of the text is looked for. The NLP can understand the main topic for a paragraph, document or sentence.
- Part of Speech Tagging: It is grammatical tagging, used to tag the part of speech used in the text.
- Intent Detection: It is the process of finding out the intent behind the text. For example, it can help businesses understand the kind of products users intend to buy.
- Word Sense Disambiguation: This uses semantic analysis to find out the words that make the most sense for a given text.
- Named-Entity Recognition: It identifies words or names as useful entities, for example, country or place names.
- Natural Language Generation: This is the opposite of speech-to-text. It puts structured information into a human language format for effective communication.
- Coreference Resolution: It involves the process of finding if and when the same words refer to the same entity.
What are the Applications of NLP?
Natural Language Processing is widely being used especially in automation and is one of the main sources behind Artificial Intelligence. It draws computer science and computational linguistics to bridge the gap between human language. Here are some of the most popular use cases of NLP in 2023:
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Customer Support/Feedback
Natural Language Processing is widely being used in automating most business processes. In fact, almost everyone has by now used customer support services that utilise NLP to respond to customers. NLP utilises data and surveys, product reviews, and social media profiles to get deeper insights into the products. Thus NLP can automatically tag customer support tickets to the right department, and use chatbots to respond to the customers.
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Spam Detection
The best spam detection software uses NLP to detect spam and phishing techniques. NLP uses languages to detect emails and understand whether the email was written by humans or not. It also checks for poor grammar, inappropriate urgency, threatening language and excessive use of financial terms, to look out for spam. Spam detection is one of the major problems that experts believe NLP has solved.
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Virtual Assistants and Chatbots
Virtual assistants like Siri and Alexa use speech-to-text to understand the voice commands of the users. They use natural language generation to understand the voice patterns and provide appropriate responses. Similarly, chatbots also work similarly. They work best to understand the human-typed text and provide even better answers. With more advancements, we can expect even better chatbots that can respond to questions in a more human-like manner.
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Machine Translation
Google Translate is one of the best examples of the most used machine translator that works on NLP. For accurate translation, the machine must understand the language, and tone, of the text. And then translate it into another language. Machine translators are getting better than before by utilizing NLP.
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Text Summaries
NLP can help in summarising huge texts from documents, pdfs, and others. Thus readers who do not have time can use NLP to create a summary of a piece of text, books, or documents. It can also be useful for researchers who do not have time to go through the entire document. This is possible with natural language generation and semantic analysis.
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Predictive Text
When using a smartphone, once you type something it automatically suggests possible words, and even words that you use frequently or have used before. The smartphone learns based on the sentences you have previously written and used. MS Word is soon going to implement this.
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Social Media Analysis
Again. NLP has become an important tool to uncover hidden data insights about social media platforms. The sentiment analysis can help in recognizing the language used on social media, feedback, review, and more accurately the hidden emotions behind a campaign, product or so on.
Final Words
At present Natural Language Processing is widely being used in communicating and automating business processes. Certainly, NLP is widely being accepted by the big names in the market, “OpenAI” being one of them. So we can expect more advancement concerning NLP shortly. We can expect to see Natural Language Processing as a part of daily routine, as computers start to learn and understand human language better. Having said that, there is still a lot of work that needs to be done to make machines understand as well as humans, and match their intelligence. You can check out emerging ai trends in 2023 and top ai tools in 2023.
If you also want to implement NLP solutions, it is good to first understand NLP and how it works. Once you have a good understanding of NLP, the next step is to approach Yn AI development company, and then get started with the project.
In this article, I have discussed NLP, how NLP works, and its most popular use cases. What is your take on NLP? Do you think it has any future and can AI write code like human intelligence? Let me know in the comments.