Transform your business capabilities with Natural Language Processing

Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand natural language – the words and sentences that humans use to communicate – and use it to create value.

While machines are great at working with and understanding structured data (such as spreadsheets and database tables), they’re not so great at deciphering unstructured data, for example, raw text in English, Polish, Chinese, or any other human language.

Natural Language Processing deals with coming up with solutions that would help bridge this gap. It enables computers to understand not just the meaning of individual words but also how human beings actually use language when we speak or type – ambiguities, colloquialisms, spelling mistakes, shortcuts, dialects, personal quirks, and more. When we teach computers to understand unstructured human language text, they can decipher what users really want when they type or speak a phrase and can extract meaningful and valuable data from it.

NLP is the driving force behind many applications we use every day. Search engines, spell checkers, translation applications, and personal assistants – almost any app or feature that involves human language uses NLP algorithms so that computers can use and understand words as people do.

NLP emerged in the 1940s as machine translation (MT). The aim of that technology was to crack Russian code during World War II, but early attempts were unsuccessful.

In the 1960s, NLP took a step forward with the introduction of SHRDLU, a language platform, and ELIZA, an early chatbot that could simulate limited conversation based on a pattern matching and substitution methodology.

Until the 1980s, many systems were based on complex hand-written rules, but at the end of the decade, NLP was revolutionized by the introduction of machine learning algorithms for language processing. These advances formed the basis of what NLP is today.

Deep learning took over in the 2010s, propelling the field of artificial intelligence forward once more and allowing NLP to achieve radically improved results.

The huge amounts of data companies have at their disposal in the digital age is a treasure trove of invaluable business insights. Using intelligent NLP methods, companies can now get the maximum value out of their data.

Analyze text data in gainful new ways

From tracking negative reviews to keeping up to date with the latest social media trends, NLP lets companies transform textual data spread across the web into actionable business insights and get ahead of the competition.

Uncover hidden patterns

Use topic modeling methods to discover customer trends or identify hidden patterns in large amounts of unstructured text, such as emails, customer reviews, social media profiles, or job applications.

Automate decisions based on text input

Streamline your processes by incorporating methods like text classification to automatically classify sentences, phrases, or even whole documents into predefined groups.

Visualize text data

Transform opaque walls of text data into meaningful and eye-catching visuals and never miss the next big trend hitting your industry or business.

Natural Language Processing is a field of machine learning. Complex systems store words and analyze the ways in which they come together – just like they would with any other type of data. Phrases, sentences, and sometimes whole books are fed into machine learning engines. These texts are then processed based on grammatical, syntactic, and other linguistic rules, as well as people’s real-life speech habits. The system then uses this data to find patterns.

The main techniques used in Natural Language Processing are syntactic analysis and semantic analysis. Syntax refers to the arrangement of words and phrases to create well-formed sentences that make grammatical sense. In NLP, syntactic analysis is used to assess how natural language aligns with grammatical (and other) rules of a language. Semantics, on the other hand, refers to the meaning that is conveyed in a given text. Semantic analysis involves applying computer algorithms to understand the meaning of words based on their context.

Combined, syntactic and semantic analysis techniques make it possible for computers to read text or hear speech, interpret words, measure sentiment, and determine which parts are important. Today, NLP software can analyze more language-based data than humans in a very short space of time, and it can do so in a consistent and unbiased way without ever getting tired. And this has great implications for businesses.

Chatbots

With natural language processing, your chatbots can be made to feel more human and help your customers get the information they need quickly, enhancing customer experience.

Voice systems

Incorporate NLP systems to recognize voice commands, and elevate customer interactions with your service and brand to a higher level. Although NLP is based on text, a ‘voice system’ uses speech recognition to turn recognized speech into text and then performs further analysis of this text based on NLP. Speech recognition and NLP are often used together, for example, in Siri and Google Assistant.

Sentiment analysis

Want to know whether tweets about your company are good or bad so that you can address your customers’ concerns? Sentiment analysis uses NLP to help businesses understand what’s being said about them on the web and social media.

Fighting spam and organizing inboxes

Spam detection uses Natural Language Processing to keep unwanted emails and other messages out of your inbox. NLP can also be used to sort messages from certain contacts into separate folders.

Machine translation

If you want to build a translation feature into your application, you’ll need Natural Language Processing. The challenging part of machine translation is not translating individual words but preserving meaning. This is a complex technical issue that’s right at the heart of NLP.

Advanced “conversational” search

Website users are human, and, like all humans, they sometimes forget some details, make spelling errors, confuse brands, or use slang or “conversational” language when running searches. NLP takes into account all these things, connects the dots, and provides accurate results that are both relevant and valuable to the customer.

Information extraction

Natural Language Processing can automatically summarize long documents or extract relevant keywords for searching. The legal industry makes use of these types of NLP applications, for example, to help lawyers sort through thousands of pages of documents in legal cases to find relevant information.

Google – Google Translate is perhaps one of the most well-known examples of companies using NLP. The system is used by half a billion people every day to convert more than 100 world languages. The company also uses NLP in its voice search.

Amazon – Alexa, Amazon’s smart voice assistant, is powered by NLP. The device is able to understand and answer questions such as “what’s on my calendar” and carry out instructions like “dim the lights.”

Coca-Cola – The soft drink giant introduced its Ask Coca-Cola virtual assistant to support its customer service department and enhance customer experience. The service now handles 30,000 conversations a month, greatly reducing the number of inbound calls and increasing efficiency in customer service.