natural language processing

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

It is not an easy task to accomplish. First, there is the problem of humans speaking concisely so that a machine can understand. Second, the problem of words that sound the same but have different meanings like weight and shape, weight and wait, etc.

How Natural Language Processing Works

Processing of the spoken or written word relies heavily on Big Data, vast amounts of structured, semi-structured, and unstructured data that can be mined for insights. Computers can quickly review the data, analyze it, and find patterns or trends. Initially, NLP was based on ground rules in which machines using algorithms were told what words and phrases to look for in the text, and then taught specific responses when the phrases appeared. It has evolved into deep learning, a flexible and instinctive method in which algorithms are used to teach a machine to identify a speaker’s intent from a series of examples.

In the evolution of NLP, algorithms have historically been bad at interpreting. However, now with improvements in deep learning and AI, algorithms can now successfully interpret.

If you own an Amazon Echo or a Google Home, then you are interacting with artificial intelligence and NLP. Furthermore, it is already being used in all kinds 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 influence five areas of healthcare.

• Clinical Data and Administrative Assistants
• Mining and data extraction
• Market analysis
• Real-time translation services

In customer service, the use of NLP can help determine customer attitudes for future sales. There will be no need for customer surveys. Instead, mining systems will offer deeper insights into a customer’s sentiments. Chatbots will allow human customer service staff to focus on other types of calls.

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

NLP and social good

NLP and AI can help prevent school shootings. For example, researchers at Columbia University have processed 2 million tweets posted by 9,000 at-risk youth to determine how language changes as a teen moves ever closer to committing a violent act.

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