Most Popular Applications of Natural Language Processing

As your team sees these trends, it would be worth learning how to respond to negative reviews and look at positive review response examples to get an idea of how to properly respond to reviews of any type. These libraries provide the algorithmic building blocks of NLP in real-world applications. Explore the possibility to hire a dedicated R&D team that helps your company to scale product development. We offer a range of NLP datasets on our marketplace, perfect for research, development, and various NLP tasks.

This significantly speeds up the hiring process and ensures the best fit between candidates and job requirements. Natural Language Processing is more than just a trendy term in technology; it is a catalyst for the development of several industries, and businesses from all sectors are using its potential. Let’s examine 9 real-world NLP examples that show how high technology is used in various industries. Voice assistants like Siri and Google Assistant utilize NLP to recognize spoken words, understand their context and nuances, and produce relevant, coherent responses. Each of these Natural Language Processing examples showcases its transformative capabilities. As technology evolves, we can expect these applications to become even more integral to our daily interactions, making our experiences smoother and more intuitive.

Generating Content

By this time, work on the use of computers for literary and linguistic studies had also started. As early as 1960, signature work influenced by AI began, with the BASEBALL Q-A systems (Green et al., 1961) [51]. LUNAR (Woods,1978) [152] and Winograd SHRDLU were natural successors of these systems, but they were seen as stepped-up sophistication, in terms of their linguistic and their task processing capabilities. There was a widespread belief that progress could only be made on the two sides, one is ARPA Speech Understanding Research (SUR) project (Lea, 1980) and other in some major system developments projects building database front ends.

Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs. The tools will notify you of any patterns and trends, for example, a glowing review, which would be a positive sentiment that can be used as a customer testimonial. Autocorrect can even change words based on typos so that the overall sentence’s meaning makes sense.

Using NLP to get insights out of documents

Though NLP tasks are obviously very closely interwoven but they are used frequently, for convenience. Some of the tasks such as automatic summarization, co-reference analysis etc. act as subtasks that are used in solving larger tasks. Nowadays NLP is in the talks because of various applications and recent developments although in the late 1940s the term wasn’t even in existence.

NLP with programming languages such as Python can help us to translate the text into different languages. These machine translations can help us to communicate with people living in different countries. OCR helps speed up repetitive tasks, like processing handwritten documents at scale. Legal documents, invoices, and letters are often best stored in the cloud, but not easily organized due to the handwritten element. Tools like Microsoft OneNote, PhotoScan, and Capture2Text facilitate the process using OCR software to convert images to text. Corporations are always trying to automate repetitive tasks and focus on the service tickets that are more complicated.

IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP.

Investors can determine whether these sources are expressing positive or negative opinions about the stock by studying the terminology used in these sources. NLP makes it extremely easy to monitor the performance of the events that you might launch on your social media handles. With NLP, you can also track the total likes, shares, and reach of your posts. Basically, they allow developers and businesses to create a software that understands human language. Due to the complicated nature of human language, NLP can be difficult to learn and implement correctly.

However, the text documents, reports, PDFs and intranet pages that make up enterprise content are unstructured data, and, importantly, not labeled. This makes it difficult, if not impossible, for the information to be retrieved by search. MonkeyLearn is a good example of a tool that uses NLP and machine learning to analyze survey results. It can sort through large amounts of unstructured data to give you insights within seconds. Similarly, support ticket routing, or making sure the right query gets to the right team, can also be automated. This is done by using NLP to understand what the customer needs based on the language they are using.

By integrating NLP into it, the organization can take advantage of instant questions and answers insights in seconds. With it, comes the natural language processing examples leading organizations to bring better results and effective communication with the customers. We provide possible solutions for wide-ranging needs like speech recognition, sentiment analysis, virtual assistance and chatbots. Natural Language Processing or NLP represent a field of Machine Learning which provides a computer with the ability to understand and interpret the human language and process it in the same manner. Since you’re acquainted with the natural language processing applications, you can now dive into the field of Natural Language Processing.

Top natural language processing examples businesses are employing

Thankfully, natural language processing can identify all topics and subtopics within a single interaction, with ‘root cause’ analysis that drives actionability. “Text analytics is a computational field that draws heavily from the machine learning and statistical modeling niches as well as the linguistics space. In this space, computers are used to analyze text in a way that is similar to a human’s reading comprehension. This opens the door for incredible insights to be unlocked on a scale that was previously inconceivable without massive amounts of manual intervention. Most recently, transformers and the GPT models by Open AI have emerged as the key breakthroughs in NLP, raising the bar in language understanding and generation for the field. In a 2017 paper titled “Attention is all you need,” researchers at Google introduced transformers, the foundational neural network architecture that powers GPT.

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