Blog, NLP, Semantic Technology

The future of natural language processing

What’s the future of natural language? NLP is a natural enabler of the intelligent, intuitive applications that most of us use every day and is transforming the way that humans and computers communicate with each other. Powered by semantic and cognitive technologies, the future of natural language processing will be able to capitalize on its potential for human-like understanding of speech and text through a variety of applications.

Natural language processing (NLP) is a form of artificial intelligence that helps machines “read” text by simulating the human ability to understand language. NLP techniques incorporate a variety of methods to enable a machine to understand what’s being said or written in human communication—not just single words—in a comprehensive way. This includes linguistics, semantics, statistics and machine learning to extract entities and relationships, disambiguate meaning and decipher ambiguities in language.

In this post, we look at the 4 areas where the future of natural language processing is becoming a reality.


The future of natural language processing: The bots

Frequently used in customer service, especially in banking, retail and hospitality, chatbots help customers get right to the point without the wait, answering customer questions and directing them to relevant resources and products at any hour, any day of the week. To be effective, chatbots must be fast, smart and easy to use, especially in a customer service context where users have high expectations (and sometimes low patience) for being understood. To accomplish this, chatbots employ NLP to understand language, usually over text or voice-recognition interactions, where users communicate in their own words, as they would speak to an agent. Integration with semantic and other cognitive technologies that enable a deeper understanding of human language will allow chatbots to get even better at understanding and replying to more complex and longer-form requests and functions in more than a single context, all in real time.

This expanded functionality will also benefit other types of bots to make them more effective and intuitive over time, from virtual assistants like Siri and Amazon’s Alexa to bot platforms that are more automation or task oriented. These bots will increasingly use NLP to understand text and perform actions such as sharing geo information, retrieving links and images or execute other more complex actions for us, and you can see some of these at work in some of the most common messaging applications.

 

The future of natural language processing: Supporting invisible UI

Human communication—both conversation and text—are part of most every interaction we have with machines. Amazon’s Echo is just one example of the move toward a future that puts humans more directly in contact with technology. The concept of an invisible or zero user interface will rely on direct interaction between user and machine, whether through voice, text or a combination of the two. NLP that leverages a greater contextual understanding of human language, in other words, as it gets better at understating us—what we say no matter how we say it, and what we are doing—will be essential for any invisible or zero UI application.


The future of NLP: Smarter search

The future of NLP is also for smarter search—something we’ve been talking about here at Expert System for a long time. Applied to search, the same capabilities that allow a chatbot to understand a customer’s request can enable “search like you talk” functionality (much like you could query Siri) rather than focusing on keywords or topics. Recently, Google announced that it has added NLP capabilities to Google Drive to allow users to search for documents and content using conversational language.

The future of NLP: Intelligence from unstructured information

An understanding of human language is especially powerful when applied to extract information and reveal meaning and sentiment in large amounts of text content (aka unstructured information), especially the types of content that must be manually examined by people. Analysis that accurately understands the subtleties of language—the choice of words, the tone used—can provide useful knowledge and insight of information, especially in the carefully worded language of annual reports, call transcripts and other investor-sensitive communications, as well as legal and compliance documents.

More effective and accurate understanding between humans and machines will only strengthen the efficiencies of both. No matter where it is applied, NLP will be essential in understanding the true voice of the user and the customer and facilitating more seamless interaction on any platform where language and human communication are used.

Learn more about three natural language processing conferences that should be on your calendar for the 2016-2017 conference season.


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