What is Natural Language Processing? An Introduction to NLP

As the number of supported languages increases, the number of language pairs would become unmanageable if each language pair had to be developed and maintained. Earlier iterations of machine translation models tended to underperform when not translating to or from English. I often work using an open source library such as Apache Tika, which is able to convert PDF documents into plain text, and then train natural language processing models on the plain text. However even after the PDF-to-text conversion, the text is often messy, with page numbers and headers mixed into the document, and formatting information lost. NLP can be used to great effect in a variety of business operations and processes to make them more efficient.

• In NLP, such statistical methods can be applied to solve problems such as spam detection or finding bugs in software code.
• To generate a text, we need to have a speaker or an application and a generator or a program that renders the application’s intentions into a fluent phrase relevant to the situation.
• NLP helps social media sentiment analysis to recognize and understand all types of data including text, videos, images, emojis, hashtags, etc.
• Insurance, pharma or legal firms which need to process large numbers of documents may well resort to NLP to extract structured information, cluster items, analyse customer support logs, or predict future events.
• So, it’s no surprise that there can be a general disconnect between computers and humans.

At the same time, there is a growing trend towards combining natural language understanding and speech recognition to create personalized experiences for users. For example, AI-driven chatbots are being used by banks, airlines, and other businesses to provide customer service and support that is tailored to the individual. Emotion detection investigates and identifies the types of emotion from speech, facial expressions, gestures, and text.

More from Sefali Warner and Artificial Intelligence in Plain English

They also try to analyze the semantic meaning behind posts by putting them into context. GPT-3 is trained on a massive amount of data and uses a deep learning architecture called transformers to generate coherent and natural-sounding language. Its impressive performance has made it a popular tool for various NLP applications, including chatbots, language models, and automated content generation.

That’s why smart assistants like Siri, Alexa and Google Assistant are growing increasingly popular. Today, NLP has invaded nearly every consumer-facing product from fashion advice bots (like the Stitch Fix bot) to AI-powered landing page bots. With Stitch Fix, for instance, people can get personalized fashion advice tailored to their individual style preferences by conversing with a chatbot. Now that we’ve explored the basics of NLP, let’s look at some of the most popular applications of this technology. To understand how, here is a breakdown of key steps involved in the process.

Statistical NLP (1990s–2010s)

We resolve this issue by using Inverse Document Frequency, which is high if the word is rare and low if the word is common across the corpus. Employee-recruitment software developer Hirevue uses NLP-fueled chatbot technology in a more advanced way than, say, a standard-issue customer assistance bot. In this case, the bot is an AI hiring assistant that initializes the preliminary job interview process, matches candidates with best-fit jobs, updates candidate statuses and sends automated SMS messages to candidates. Because of this constant engagement, companies are less likely to lose well-qualified candidates due to unreturned messages and missed opportunities to fill roles that better suit certain candidates. Just like you, your customer doesn’t want to see a page of null or irrelevant search results. For instance, if your customers are making a repeated typo for the word “pajamas” and typing “pajama” instead, a smart search bar will recognize that “pajama” also means “pajamas,” even without the “s” at the end.

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