Natural Language Processing

It wasn’t long after someone came up with the idea of ​​a robot that people wanted it to understand human speech and text. It was a dream that could only be found on the pages of science fiction books and short stories, or observed in movies. Known as Natural Language Processing (NLP), the concept of computer understanding of human speech and text is now here.

It is not an easy task to accomplish. First, there is the problem of human speech in a concise way so that a machine can understand. Second, the problem of words that sound the same but have different meanings such as roads and manner, weight and wait, etc.

This is how natural language processing works

Processing the spoken or written word relies heavily on Big Data, large amounts of structured, semi-structured and unstructured data that can be extracted for information. Computers can quickly review the data, analyze it and find patterns or trends. Initially, NLP relied on basic rules, where machines using algorithms were told which words and phrases to look for in text and then learned specific answers when the sentences appeared. It has evolved into deep learning, a flexible, more instinctive method where algorithms are used to teach a machine to identify a speaker’s intent from a variety of examples.

In the development of NLP, algorithms have historically been poor to interpret. But now with improvements in deep learning and AI, algorithms can now successfully interpret.

If you own an Amazon Echo or a Google Home, you interact with artificial intelligence and NLP. In addition, it is already used in all sorts of business applications, including manufacturing, business analytics, customer relations, human resources and healthcare.

NLP, AI and companies

In the coming years, natural language processing and artificial intelligence will have an impact on five health areas.

  • Clinical data and administrative assistants

  • Data mining and extraction

  • Market analysis

  • Real-time Translation Services

In customer service, the use of NLP can help determine customers’ attitudes toward future sales. No customer surveys will be needed. Instead, mining systems will provide deeper insights into a customer’s emotions. Chatbots allow human customer service professionals to concentrate on other types of calls.

NLP will help human resource departments recruit job seekers, make it easier to sort through resumes, attract more specific candidates, and hire more qualified workers. NLP in spam detection keeps unwanted emails out of a practitioner’s inbox. It can also be used to “read” tweets and determine if they are good or bad for a company so that customer concerns can be addressed.

NLP and social good

NLP and AI can help prevent gunshots. For example, Columbia University researchers have processed 2 million tweets sent by 9,000 youth at risk to determine how language changes as a teenager gets closer and closer to committing a violent act.

There are so many uses for NLP now, and as the technology expands, no doubt more can be achieved.