Natural Language Processing (NLP) – a branch of Artificial Intelligence aimed at empowering computers to process and analyze human written/spoken language.
The goal of NLP is to make a computer understand and be able to manipulate human language. For this, we can divide this area into a set of tasks. For example, optical character recognition (OCR), natural language generation, word sense disambiguation, speech recognition and many others.
Where do we encounter NLP?
Natural language processing have already enhanced many applications. Automatic correction in text editors, spam filters in email, virtual voice assistant (Siri, Alexa, Google Assistant), machine translations (Google Translate) and so on.
BETTER MANAGE YOUR COMPANY’S DATA
Extract Relevant Information
Sifting through high volumes of data is quite a difficult and tedious task. Sometimes it’s extremely hard to find some specific, important piece of information among the whole data set. Luckily, we can delegate such a task to a computer.
One example where the feature proved relevant is the insurance fraud detection task. Basically, by making use of NLP and Machine Learning techniques, a fraud detection system automatically revises the submitted insurance forms, checks for keywords, word/phrase triggers, and then tells its verdict.
Question Answering feature
A Question Answering (QA) application is a system capable of coherently answering a human written or spoken request. For each large repository of data, data should be found via search.
An NLP-enhanced application can identify, classify, tag, and correctly assign documents to thematic groups. Grouping thematically coherent documents is becoming more vital for today’s organizations, as storing good document structure is essential for performing wide and deep analysis.
Summarization
In the days of the ever-growing amount of information, there emerges the need to navigate through large quantities of data more efficiently and consume relevant information faster. In this sense, automatic text summarization becomes extremely significant. The key idea of it is to transform large pieces of data into short comprehensive summaries.
GET TO KNOW HOW PEOPLE TREAT YOUR BRAND AND PRODUCTS
Do people like or dislike your brand? What do they think about your product in particular? The answers are even not to be asked. People share their thoughts on the Internet, so all one has to do is simply to collect this info.
As the amount of data is constantly growing, the algorithms become more precise and the costs on software decrease, more and more companies start using tools, e.g. Business Intelligence systems that provide insights on what’s going on inside the company. But it’s also not less important to be aware of what’s going on outside the company.
Companies leverage external data, which comprises various blog posts, forum threads, social network posts, news etc, for reaping diverse benefits. With external data, a company can learn much about their target audience, gauge public perception of a company brand image, and enhance decision-making.
In this sense, sentiment analysis is gaining a role. We perform sentiment analysis to discover the positive, neutral or negative attitude toward some event/thing. It can go even beyond by revealing the type of the author’s emotion, such as anger, irritation, admiration, curiosity, and others. It is especially useful in monitoring posts from social media.
Sentiment analysis tools can provide crucial insights to a company by delivering the true data on the people’s feelings, attitude and emotions toward its brand, products and services. Moreover, stock market players also can use such a software. It’s well known that changes in sentiment on social media correlate with the stock price changes.
CONVERSATIONAL UI TO ATTRACT CLIENTS
Technology and devices play significant roles in our daily life. Obviously, with technology processes workflow becoming more advanced and widespread, the development community attempts to make software solutions as seamless and easy-to-use for people, as possible. One of the goals to achieve is that consumers should be able to interact with technology through their natural language.
Think of how we interact with computers now. Basically, we are accustomed to clicking on links and buttons, opening windows, icons, different kinds of menus and forms to enter and retrieve information. We are really accustomed to such navigation and don’t ever come to think of it (unless you are a UX/UI designer). But even though, there is another way of a “human-computer” interaction which has fewer barriers in between, requires even less effort and is most common to people. This is known as conversation (yes, a common conversation, like that one we have with other people).
Conversational user interface (UI) has been a trend for a while, which is about to continue. Currently, the most common applications of conversational UI are virtual assistants, such as Siri in iOS, Cortana, or Amazon’s Alexa. Another application is a chatbot.
Chatbots can become really of help to businesses across many industries. Here is a list of those which continue to benefit most from chatbots: retail, dining, delivery services, news & media, fashion & beauty, travel and real estate. Many of them have already adopted chatbots as a new distribution channel, customer service representative, or as a translator of their brand image.
SUMMING UP
How would we describe the modern day NLP capabilities? So on the one hand, computers are not able to produce natural language processing applications in business at the appropriate level yet. At least for now. But on the other hand, there are prominent, and what is more important – sufficient – advancements in the sphere.
Even today, a huge number of companies can benefit by:
- Better Manage Your Company’s Data
- Getting to know how people treat your brand and products
- Applying Conversational UI to satisfy Customers
