What Is Natural Language Processing?
Natural language processing is the technology used to enable computers to understand natural language, i.e. words and sentences, that humans use to communicate.
It’s not easy for computers to do this. While they are great at working with and understanding structured data (such as spreadsheets and database tables) they’re not so great at deciphering unstructured data, e.g. raw text in English, Polish, Chinese, or any other human language.
Natural language processing is about bridging this gap. It enables computers to understand not just the meaning of individual words, but how human beings actually use language when we speak or type – ambiguities, colloquialisms, spelling mistakes, shortcuts, dialects, personal quirks, and all. 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.
How Natural Language Processing Works
Natural language processing works through machine learning. Machine learning systems store words and analyse 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 the 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 context.
Combined, syntactic and semantic analysis techniques make it possible for computers to read text or hear speech, interpret the words, measure sentiment, and determine which parts are important. Today, NLP software can analyse 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.
How Natural Language Processing Can Be Used
Chatbots
With natural language processing, your chatbots can be made to feel more human, and help your customers get the information they need more naturally.
Voice Systems
When text is not enough, you can incorporate NLP systems to recognise voice commands, elevating customer interactions with your service and brand to a much higher level.
Sentiment Analysis
Want to know whether tweets about your company are good or bad so that you can address customer concerns? Sentiment analysis uses NLP to help businesses understand what’s being said about them on the web and social media.
Fighting Spam and Organising Inboxes
Spam detection uses natural language processing to keep unwanted email 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 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 details, make spelling mistakes, confuse brands, or use slang or “conversational” language when conducting searches. NLP takes into account all these things, connects the dots, and provides accurate results that are both relevant and valuable for the customer.
Information Extraction
Natural language processing can automatically summarise 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.
Why Companies Should Use NLP
The huge amounts of data companies have at their fingertips in the digital age are a treasure trove of invaluable business insights. Using intelligent NLP methods, companies can now get the most value out of their data.
Analyse 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 text data spread across the web into actionable business insights and get ahead of the competition.
Uncover Hidden Patterns
Utilise topic modelling 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.
Automatic Decisions Based on Text Input
Streamline your processes by incorporating such methods as text classification to automatically classify sentences, phrases, or even whole documents into predefined groups.
Text Data Visualisation
Transform opaque walls of text data into meaningful and eye-catching visuals and never miss the next big trend hitting your industry or business.
Improving Text-Based Searches and Classification with NLP
Forget about traditional search functions based only on keyword occurrence. By making use of context you can understand user intent with NLP and ensure your customers always find precisely what they’re looking for.
Context-Aware Search
Make your search engine feel intelligent and let your users express what they are looking for in the most natural way. With NLP, search processing deciphers what your customers really mean to deliver the most relevant results.
Extract Information to Improve Searches
Enable more intuitive search filtering by intelligently extracting information – such as named entities, facts, and phrases – from content.
Use the Past to Enhance the Future
Enhance the customer journey and deliver personalised recommendations based on users’ on-site behaviour and previous search habits.