Recently, natural language processing (NLP) artificial intelligence has matured to the point that it is challenging to discern if you’re communicating with a robot or a human if you’re not face-to-face. Getting NLP to this point was an incredible feat and one that was made possible by advances in machine learning and allowed businesses to leverage it in countless ways.
One of the most exciting—and challenging—developments in artificial intelligence was to figure out how machines could generate, and process language like humans do. A task that humans take for granted each and every day turned out to be a complex problem for machines to tackle. It wasn’t until machine learning became more widespread that machines could have “conversations” similarly to us humans. Today, it can be hard to detect that you might be in communication with a machine rather than a human.
Natural language processing (NLP)
In natural language generation (NLG), machines extrapolate insights from text and write or speak it in conversational language. It is used to improve operational efficiency and human productivity. Consider NLG as the writer and natural language processing to be the reader of the content that NLG creates.
Natural language processing isn’t error-free, but it’s certainly come a long way and is in widespread use today.
How Do Machines Understand Human Language?
Human communication isn’t as simple as knowing the definitions of words. It involves not only the words we choose to speak or write, but also our tone, context, body language and more. Getting machines to speak, write and understand human language in a seamless way hasn’t been easy. However, today, natural language processing and generation have become so sophisticated; it can be hard to discern if you’re speaking to a machine or human.
Human communication is made from unstructured data and is a lot messier than the row, and column structure machines had become adept at understanding. With the development of machine learning and natural language processing, machines cognitively understand the nuances of human language, including sentiment analysis.
The algorithms that make natural language processing work rely on a variety of techniques, including rule-based, statistical and machine learning methods. Ultimately, what occurs in natural language processing is the machine breaks down the language into elemental pieces sort of like how you may have diagrammed sentences back in elementary school. It’s looking to understand the relationships between the elements.
How is Natural Language Processing Used Today?
Do you have a virtual assistant such as the Amazon Echo or Google Home that served as your alarm this morning?
Maybe you used Siri to get directions to your destination when driving to an appointment?
These are just some of the examples of the conversational interface that operate in our everyday lives thanks to natural language processing. Machines can outpace humans when analysing language-based content.
Businesses use chatbots prolifically to support the customer experience. Most websites that sell a product or service use an AI chatbot. These bots are able to quickly and efficiently solve many customer issues because the algorithms are able to understand human language and respond appropriately. They continually learn from experience and become better over time. Companies who use chatbots not only save time and money on human resources, they can enhance the customer experience as long as the human gets the answer they need or a resolution to their issue. Machines can aggregate their experience, and therefore, the learning accelerates faster than when you think of things in terms of one conversation at a time. In essence, every conversation that takes place between a human and a machine can be used to improve the algorithm’s performance over time.
Chatbots are just the tip of the iceberg for how businesses can leverage the power of natural language processing. In the future, it’s expected that chatbots will be able to craft marketing messages, propose strategy and tactics based on what they learned was useful in the past.
Natural language processing has valuable uses across many industries. In healthcare, Amazon Comprehend Medical uses natural language processing to pull out important medical information from unstructured text such as from doctor’s notes, patient health records and more. This ability over a large number of cases could even support medical research and the prediction of diseases in the future.
Companies can also use natural language processing to help filter out resumes when recruiting talent. The algorithm can sort through preferred skills, certifications and qualifications before any human has to spend any time determining who might be worth a callback. This means job-seekers must pay close attention to aligning their resumes with the job requirements to make it through the AI hurdle.
There are countless use cases for natural language processing in business today that can help improve operations, customer service and even help sort through product development considerations. All you need to do is think of tasks and activities where human communication is involved. It’s those tasks that more than likely could be handled by natural language processing and the right algorithm.