4 Simple Ways Businesses Can Use Natural Language Processing

Google offers an elaborate suite of APIs for decoding websites, spoken words and printed documents. Some tools are built to translate spoken or printed words into digital form, and others focus on finding some understanding of the digitized text. One cloud APIs, for instance, will perform optical character recognition while another will convert speech to text. Some, like the basic natural language API, are general tools with plenty of room for experimentation while others are narrowly focused on common tasks like form processing or medical knowledge.

Natural Language Processing combats manual text analysis

If we’re not talking about speech-to-text NLP, the system just skips the first step and moves directly into analyzing the words using the algorithms and grammar rules. Some company is trying to decide how best to advertise to their users. They can use Google to find common search terms that their users type when searching for their product. NLP is an emerging technology that drives many forms of AI you’re used to seeing.

How do AI scientists build models?

You can even ‘hand build’ a chatbot in Facebook Messenger to act as an autoresponder. Platforms like Drift and Intercom are typical, offering automated response platforms that can also gather information about your visitors. The search engines have become adept at predicting or understanding whether the user wants a product, a definition, or a pointer into a document. This classification, though, is largely probabilistic, and the algorithms fail the user when the request doesn’t follow the standard statistical pattern.

How Artificial Intelligence Is Used in Customer Service

Nori Health intends to help sick people manage chronic conditions with chatbots trained to counsel them to behave in the best way to mitigate the disease. They’re beginning with “digital therapies” for inflammatory conditions like Crohn’s disease and colitis. Shield wants to support managers that must police the text inside their office spaces. Their “communications compliance” software deploys models built with multiple languages for  “behavioral communications surveillance” to spot infractions like insider trading or harassment. Google, Netflix, data companies, video games and more all use AI to comb through large amounts of data. The end result is insights and analysis that would otherwise either be impossible or take far too long.

AI in training: FATE develops digital doctor’s assistant

For example, suppose a dataset has language that assigns certain roles to men, such as computer programmers or doctors but assigns roles, like homemaker or nurse, to women. In that case, the AI program will implicitly apply those terms to men and women when communicating in real time. Therefore, stereotypes existing within the data set can lead to algorithms having language that applies unfair stereotypes based on race, gender, and sexual preference. As NLP capabilities demonstrated significant progress during the last years, it has become possible for AI to extract the intent and sentiment behind the language. This can be used to derive the sentiment of conversations with individual customers and steer the conversation towards a conversion, as with the Vibe’s Conversational Analytics platform.

• These systems can reduce or eliminate the need for manual human involvement.
• Tools such as MeaningCloud and ML Analyzer can automatically summarize long documents into short, fluent, and accurate summaries.
• Political bias is another real concern for natural language processing programs that may lead to the impression of information based on the political preference of the data set used to train the AI.
• It takes many forms, but at its core, the technology helps machine understand, and even communicate with, human speech.
• AI scientists hope that bigger datasets culled from digitized books, articles and comments can yield more in-depth insights.
• Thanks to AI technologies such as machine learning, coupled with the rise of big data, computers are learning to process and extract meaning from text – and with impressive results.

I might not touch on every technical definition, but what follows is the easiest way to understand how natural language processing works. Every day, humans say thousands of words that other humans interpret to do countless things. At its core, it’s simple communication, but we all know words run much deeper than that. There’s a context that we derive from everything someone says. Whether they imply something with their body language or in how often they mention something. While NLP doesn’t focus on voice inflection, it does draw on contextual patterns.

Another use case for NLP in marketing lies in the area of relevant news aggregation. The state-of-the-art text summarization approaches enable marketers to extract relevant content about their brand from online news, articles, and other data sources. EWeek has the latest technology news and analysis, buying guides, and product reviews for IT professionals and technology buyers. The site’s focus is on innovative solutions and covering in-depth technical content. EWeek stays on the cutting edge of technology news and IT trends through interviews and expert analysis.

Services such as Otter and Rev deliver highly accurate transcripts—and they’re often able to understand foreign accents better than humans. In addition, journalists, attorneys, medical professionals and others require transcripts of audio recordings. NLP can deliver results from dictation and recordings within seconds or minutes. In every instance, the goal is to simplify the interface between humans and machines.