How Natural Language Processing Is Used
As popular as Natural Language Processing is becoming, not everyone truly understands what it is and how it can improve efficiency.
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Natural Language Processing has become yet another buzzword in the past few years. But not everyone really understands what NLP is and how it can be used to improve efficiency.
What Is Natural Language Processing?
Let’s start with the basics. Natural Language Processing (NLP) is the ability of a computer program to understand human speech as if it were spoken. It is a component of Artificial Intelligence (AI), another big trend these days.
In other words, Natural Language Processing is a field of computer science, AI, and computational linguistics concerned with the interactions between computers and human languages. It is a computer activity in which computers analyze, understand, and generate natural language. This includes the automation of any or all linguistic forms, activities, or methods of communication, such as conversation, correspondence, reading, written composition, publishing, translation, lip reading, and so on.
In fact,NLP is one aspect of Machine Learning, Big Data, and Artificial Intelligence that has the potential to truly change everything.
How Natural Language Processing Is Used
Have you ever talked with your computer or smartphone? Just a few years ago, that question would have come from a science fiction film. But with advances in NLP, you can now ask your phone to send a text message to a specific person, and it does for you. Siri is the most obvious example. But let’s look at other examples that may help your business boom!
Many important decisions in financial markets are increasingly moving away from human oversight and control. Algorithmic trading, a form of financial investing that is entirely controlled by technology, is becoming more popular. But many of these financial decisions are impacted by news that is still presented predominantly in English. A major task, then, of NLP has become taking these text announcements and extracting the information into a format that can be put into algorithmic trading decisions.
For example, news of a merge between companies can have a big impact on trading decisions, and the speed at which the the mergers, players, prices, who acquires who, and more can be incorporated into a trading algorithm and can bring great profit.
You may also have used NLP if you have ever used the “translate” link on Facebook to translate a foreign language into your own. Google is a company at the forefront of machine translation using a proprietary statistical engine for its Google translate service. The challenge with machine translation technologies is not in translating words but in preserving the meaning of sentences, a complex technological issue that is at the heart of Natural Language Processing.
Another use of NLP is text classification. Google and other email providers use it to determine if an email is spam or not. Spam filters have become important as the first line of defense against the unwanted email.
Other NLP programs are being developed and used that can automatically summarize long documents or extract relevant keywords for searching. The legal system uses these types of applications, for example, to help lawyers sort through thousands of pages of documents in any given legal case to find relevant information.
Information overload is a real phenomenon in our digital age, and already, our access to knowledge and information far exceeds our capacity to understand it. This is a trend that shows no sign of slowing down, so the ability to summarize the meaning of documents and information is becoming increasingly important.
Marketers are using NLP for sentiment analysis, combining millions of tweets and other social media messages to determine how users feel about a particular product or service. It has the potential to turn all of Twitter or Facebook into one giant focus group.
Search engines put a lot of information at our fingertips but they are still generally quite primitive when it comes to answering specific questions asked by humans. It is getting better and better year by year. Also, companies are predicting that chatbots, another growing trend, will be able to take over some customer-service functions in as little as five years, providing automated, real-time responses to simple customer-service problems and questions.
Benefits of NLP for On-Site Search
One of the most popular usages of Natural Language Processing is on-site search. Online shoppers have noticed how much e-commerce has evolved over the years and how online shopping is a far less frustrating experience than it was just a few years ago, but for the majority of shoppers, the technological changes that are taking place behind the scenes are invisible.
The adoption of Natural Language Processing is one of the key drivers of change in e-commerce today, and the impact that these technologies are having on customer experience is both far-reaching and powerful. So, here you will find key benefits of NLP and learn why it is better than the stock search algorithm and other traditional keyword- and text-based searches.
Semantic search provides results that are exactly what your customers are looking for.
Search Processing Deciphers What Customers Really Mean
Your customers are human. They make spelling errors, confuse brands with products, and forget details. NLP bridges the gap when these errors occur. NLP connects the dots to keep search seamless.
More Data Mined Means More Data for Growth
Measuring what your customers are searching is key in improving your business. Through the tremendous depth of data presented by NLP, you’re able to analyze the data to a huge degree, learning about customer habits and tendencies across your entire consumer base. This data can be applied from merchandising to SEO, marketing campaigns to sales, etc.
Complex Search Capabilities Eliminate Ineffective Results
Natural Language Processing looks at the whole picture, not just the individual keywords in a search, providing results that are the sum of their parts. Results might be wrongly identified by text-based searches or accidently omitted from keyword queries.
Companies like Peerius, NOSTO, and RichRelevance use Machine Learning together with NLP to enhance the shopping journey. These providers use data to continue to provide recommendations based on onsite behavior and previous search habits.
Obviously, if a customer uses on-site search and is presented with a random assortment of irrelevant products — or even worse, the No Results Found page — that customer is likely to leave the site and a potential sale is lost. The damage done goes much further, however, as that customer is unlikely to return for future purchases. If the customer leaves a site after a poor experience and subsequently visits a competitor’s site where the on-site search delivers fast and accurate results, it’s not difficult to guess where the customer’s loyalty will lie when it comes to future shopping. It’s vital to understand the lifetime value of customers when weighing up the benefits of investing in NLP-based on-site search.
Moreover, Gartner predicts that by 2020, 85% of customer interactions will be managed without human intervention.
For example, Amazon’s Echo is currently at the front of the NLP pack with voice recognition, which is the next step in reproducing the experience of interacting with a human representative. Shoppers who have purchased Echo increase their spending by 10 percent overall, with half of this increase going directly to Amazon products.
Stay tuned for next time, when we'll look at the top Natural Language Processing startups and influencers of 2017!
Published at DZone with permission of Ekaterina Novoseltseva. See the original article here.
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