What is Natural Language Processing NLP?

You can easily appreciate this fact if you start recalling that the number of websites or mobile apps, you’re visiting every day, are using NLP-based bots to offer customer support. Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding. As mentioned earlier, virtual assistants use natural language generation to give users their desired response. To note, another one of the great examples of natural language processing is GPT-3 which can produce human-like text on almost any topic.

NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence. It is the technology that is used by machines to understand, analyse, manipulate, and interpret human’s languages. It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation. Branched out of artificial intelligence (AI), natural language processing (NLP) works on communication between humans and machines. It primarily focuses on how can a computer be programmed to understand, process and generate language like a human. NLP powers intelligent chatbots and virtual assistants—like Siri, Alexa, and Google Assistant—which can understand and respond to user commands in natural language.

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In doing so, the algorithm can identify, differentiate between and hence categorise words and phrases and therefore develop an appropriate response. Some of the most common NLP examples include Spell Check, Autocomplete, Voice-to-Text services as well as the automatic replies system offered by Gmail. A subfield of NLP called natural language understanding (NLU) has begun to rise in popularity because of its potential in cognitive and AI applications. NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate well-formed human language on its own.

In the beginning of the year 1990s, NLP started growing faster and achieved good process accuracy, especially in English Grammar. In 1990 also, an electronic text introduced, which provided a good resource for training and examining natural language programs. Other factors may include the availability of computers with fast CPUs and more memory.

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Many large enterprises, especially during the COVID-19 pandemic, are using interviewing platforms to conduct interviews with candidates. These platforms enable candidates to record videos, answer questions about the job, and upload files such as certificates or reference letters. Although machines face challenges in understanding human language, the global NLP market was estimated at ~$5B in 2018 and is expected to reach ~$43B by 2025. And this exponential growth can mostly be attributed to the vast use cases of NLP in every industry. Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones. But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do.

At its core, AI is about creating machines that can perform tasks that would typically require human-level intelligence. NLP helps to enable this by allowing computers to understand and interact with human language, which is a crucial part of many AI applications. Given the many applications of NLP, it is no wonder that businesses across a wide range of industries are adopting this technology. For example, chatbots powered by NLP are increasingly being used to automate customer service interactions. By understanding and responding appropriately to customer inquiries, these conversational commerce tools can reduce the workload on human support agents and improve overall customer satisfaction. The process is known as “sentiment analysis” and can easily provide brands and organizations with a broad view of how a target audience responded to an ad, product, news story, etc.

To understand which NLP language model will help your project to achieve maximum accuracy and reduce its time to market, you can connect with our AI experts. GPT-3 is a transformer-based NLP model that performs translation, question-answering, poetry composing, cloze tasks, along with tasks that require on-the-fly reasoning such as unscrambling words. Moreover, with its recent advancements, the GPT-3 is used to write news articles and generate codes.

If you’re interested in learning more about how NLP and other AI disciplines support businesses, take a look at our dedicated use cases resource page. NPL cross-checks text to a list of words in the dictionary (used as a training set) and then identifies any spelling errors. The misspelled word is then added to a Machine Learning algorithm that conducts calculations and adds, removes, or replaces letters from the word, before matching it to a word that fits the overall sentence meaning. Then, the user has the option to correct the word automatically, or manually through spell check. Sentiment analysis (also known as opinion mining) is an NLP strategy that can determine whether the meaning behind data is positive, negative, or neutral.

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Subcategories include NLG (Natural Language Generation) and NLU (Natural Language Understanding). According to Statista, the NLP market is projected to grow almost 14 times larger by 2025 compared to its market size in 2017. It is equivalent to a boost from around 3 billion USD in 2017 to more than 43 billion in 2025. However, before proceeding to the real-world examples of NLP, let’s look at how NLP fares as an emerging technology in terms of stats. In this section, you will get to explore NLP github projects along with the github repository links.

Syntactic analysis involves the analysis of words in a sentence for grammar and arranging words in a manner that shows the relationship among the words. Natural Language Processing allows your device to hear what you say, then understand the hidden meaning in your sentence, and finally act on that meaning. But the question this brings is What exactly is Natural Language Processing? For processing large amounts of data, C++ and Java are often preferred because they can support more efficient code.

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