AI 101: The Basics of Natural Language Processing

As humans, everything we express, whether verbally or written, holds enormous amounts of information. The subject we choose, our selection of words, the tone, and pretty much everything else adds some information that can be socially interpreted and value extracted from it. Theoretically, we can understand and even predict human behavior by utilizing that information.

But there’s one slight problem: one person may produce thousands of words in a single statement, each sentence with its own analogous complexity. If you want to analyze hundreds, thousands, or millions of people and their statements in a given region, the situation becomes unmanageable.

Data that are generated from random dialogues or even tweets is a fine example of unstructured data. This unstructured data doesn’t suit the conventional row and column framework of relational databases. It also represents the vast majority of data available in the world we live in. Unstructured data is messy and hard to manipulate.

Even so, thanks to advances in disciplines like machine learning, a massive revolution is currently underway regarding this topic. These days it’s no longer about trying to decipher a speech or text based on its keywords but about fully understanding the meaning behind those words. Enter natural language processing.

Photo from Forbes

Natural language processing, often abbreviated as NLP, is a subcategory of artificial intelligence that gives the machines the capability to read, understand, and most importantly, derive meaning from human languages. It’s a discipline that concentrates on the interaction between data science and human language and is scaling to nearly all modern-day industries.

In 2021, natural language processing is flourishing thanks to the massive improvements in data access and the rise of computational power, allowing organizations to achieve significant results in a handful of industries. In this blog post, first, we’ll explain what natural language processing is, how it works, its most common applications and showcase real-time examples of companies enhancing their business processes with the help of NLP. Without further ado, here’s everything that you should know about the basics of NLP.

Introduction To Natural Language Processing

Defining NLP

Natural language processing is a  subfield of artificial intelligence technology that deals with training a computer to register, understand, process, and generate human languages. Whereas the term NLP initially referred to a system's ability to read, it's since matured into a colloquialism for all computational linguistics.

Subcategories comprise natural language generation, or NLG—the computer's capacity to create communication of its own, and natural language understanding, or NLU—the ability to learn slang, misspellings, mistakes, and other variables in human linguistics.

Natural language processing's groundbreaking technology powers machine translation services, chatbots, speech recognition, spam detection, document summarization, autocomplete, voice assistants, search engines, predictive typing, and many other intelligent systems.

In fact, most of us have smartphones that have speech recognition integration, as these devices use NLP to understand what is said. Additionally, most of the laptops and computers we use run on operating systems with built-in speech recognition, so natural language processing is everywhere around us if you think about it.

How Does It Work?

Natural language processing mainly works through another application of AI called machine learning, or ML, that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML concentrates on the evolution of computer programs that can access data and utilize it to learn for themselves.

Put into practice, machine learning systems stock words and the ways they come together, just like any other type of data. Sentences, phrases, expressions, and sometimes full books are put into machine learning engines where they’re processed using grammatical rules and people’s real-world linguistic habits. The engines then use this data to discover patterns and recognize what comes next.

Let’s take translation software, for example. In German, “I’m going to the cinema” is “Ich gehe ins Kino,” so ML engines anticipate that “I’m going to the park” will also begin with “Ich gehe ins.” After that, all the computer needs is the German word for “park.”

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Natural Language Processing Applications

As the human dependency on computing systems to communicate and carry out specific tasks increases, natural language processing applications are both gaining momentum and attention. Here are a few prominent examples of how NLP is already incorporated into our everyday lives.

Email Filters

Email filters represent one of the most basic natural language processing applications there are. Email filters have started with spam filters, exposing certain words or expressions that signal a spam message. However, spam filtering has upgraded over the years, just like all early adaptations of NLP.

One of the most common, newer natural language processing applications is found in Gmail’s email classification, where Gmail’s system recognizes if received emails belong in one of the three main classifications (primary, social, or promotions) established on their contents. This helps all Gmail users keep their inbox clean and more manageable, with important and relevant emails segregated from the others.

Photo by Stephen Phillips from Unsplash

Chatbots And Smart Assistants

Chatbots and smart assistants wouldn't be here without the power of natural language processing either. By perceiving your input, chatbots and intelligent assistants can provide you with a quick answer that satisfies your request. Moreover, recent NLP and machine learning advancements have further helped chatbots understand misspellings and shorthand to recognize the context behind people's inputs to a limited extent. For example, sophisticated chatbots and assistants understand that "asap" means "as soon as possible" or that "ty" indicates "thank you" and isn't a misspelling of the word "tie."

Nevertheless, the current approach to conversational AI with the existing state of NLP technology is not sufficient for the complexity of human conversations. Most current chatbot templates and solutions are somewhat limited in their capacities, mainly because they are rules-based and lack the backing of suitable algorithms.

For instance, the issues of intent and context limitations are often overlooked when an organization architects its bot solutions. If you want to invest in a “next level” customer service solution that will serve your business’s needs for the years to come, go with a context-aware machine learning-based approach and harness machine learning within the architecture of your solution to understand better the context of what a customer is saying.

Search Results

Search engines like Google, Bing, and Yahoo all utilize natural language processing to surface relevant results based on similar search behaviors so that the average person finds precisely what they need without being a search virtuoso.

For instance, Google not only forecasts what widespread searches may apply to your search as you start typing on your keyboard, but it looks at the big picture and acknowledges what you’re trying to find rather than your exact search keywords.

Photo by Nathana Reboucas from Unsplash

Language Translation

One of the most indicating signs that you’ve cheated on your German homework assignment is that in most cases, grammatically—it’s a mess. Numerous languages don’t permit straight translation and have diverse orders for sentence structure, which translation services used to neglect—used to because, through natural language processing applications, they’ve certainly come a long way.

With natural language processing, online translation services can accurately translate languages and offer grammatically correct outcomes. This NLP application is particularly beneficial when you’re trying to communicate with someone in another language.

Natural Language Processing Examples

Data comes in countless forms. However, the largest untapped pool of data consists of text as product specifications, academic publications, patents, news, social media feeds, and others all have text as the main component. Besides, the volume of text is constantly growing and shows no signs of slowing down. Here are a couple of fascinating examples of how organizations and enterprises are putting natural language processing technology to work.

Processing Customer Requests

Verizon heavily utilizes natural language processing and deep learning to automate the processing of customer request comments as they receive more than 100,000 requests per month. Their AI-powered customer service system reads repair tickets and automatically responds to the most common requests, such as reporting on repair progress updates or current ticket status.

Y Meadows’ AI and NLP-powered customer service automation software can help you bridge the gap between your customer service agents and ticketing systems to enhance the overall customer journey experience similarly by accelerating ticket resolution times and supercharging your customer support team.

NLP For Analyzing Contracts

Accenture’s Legal Intelligent Contract Exploration tool assists the company’s legal organization of more than 2,500 professionals in performing text searches across its million-plus contracts, including searches for contract clauses.

This tool utilizes word embedding to search through contract documents paragraph by paragraph, looking for specific keywords to identify if the section relates to a particular contract clause type.

Final Thoughts

Whether you realize it or not, natural language processing is altering how businesses analyze and interact with language-based data by teaching computers to make sense of speech and text and perform automated assignments like classification, summarization, translation, extraction, and others.

Photo from Medium

Not that long ago, the idea of computing systems capable of understanding human language seemed inconceivable. Be that as it may, in a relatively short time, fueled by research and development in technology and linguistics, computer science, and machine learning—natural language processing has become one of the fastest-growing and most promising fields within artificial intelligence.

And as technology is advancing, natural language processing becomes more accessible than ever. Thanks to NLP-powered software, now you can create customized solutions that will help automate processes and better understand customers within your business's scope of work.

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