Natural Language Processing(NLP) uses artificial intelligence to read, comprehend and create meaning from human languages. The input for NLP can be spoken or written. Even if you’re not familiar with how NLP functions, you have probably heard about some of the growing pains it has experienced. Chatbots from leading companies like Microsoft and Facebook have been discontinued after notorious fails.
While there have been some real public mishaps with NLP, its recent successes far outnumber failures. AI writers and customer service bots are widespread now, fulfilling needs in areas as diverse as medicine and law. Your phone almost certainly has a digital assistant that helps you do everything from scheduling to translation.
NLP has now become a comprehensive solution to many communication needs. The process of converting human speech and written communication into usable data is in continual development. At this time, some of the key industries making the best use of NLP are healthcare, financial trading, email filtering, and search.
There are several key concepts that support NLP, providing useful data from the complexity of human language. These algorithms are separated into a number of categories:
These algorithms, while not an exhaustive list of the processes available underlie much of the NLP revolution. Through the use of these algorithms, relationships between words, building contextual meaning that can be applied to data-driven customer-facing solutions. This helps to provide real answers to customer inquiries or to provide improvements to its main tasks. As this industry does rely on data, the ability to process more speech or text over time has inevitably led to improvements, and this trend will continue.
There are many challenges left to overcome. Language is very abstract, and differences in dialect and origins create problems in standardization. Detection of irony and sarcasm continues to be really difficult along with other complexities like metaphorical speech. Applying this in tasks like machine translation is still something that remains limited. But advances are moving along rapidly.
Applications like spell checking and basic chatbots have been around for well over 50 years now, and their capabilities go far beyond what humans can attain with editing and response times. These more simple tasks are now accepted as being the domain of NLP, but its applications are endless. Machine translation, speech recognition, text summary, and market analysis continue to advance rapidly in offering rapid solutions for daily tasks.
It is now a booming time to get more involved in NLP and utilize its incredible potential for business, better governance, and risk management.