Natural Language Processing – What it is and why it’s important

Introduction

Natural Language Processing — the application of software systems to examining, interpreting and accurately responding to speech is viewed as the next big leap in user interface technology. However, human speech is far more complex than most people realize. There are rules, such as spelling and grammar. How we interpret speech and text, though, is far less well-defined. How do you know when a person is being sarcastic, for example? How do we know that an athlete’s explosive sprint to the finish line didn’t involve any pyrotechnics? In human language, the words can say one thing, but the context and the tone make those words mean something else.

It takes humans a half a lifetime to learn the subtle nuances of language. Even then, there will be words and phrases that some of us don’t understand. Then, there are further complications in understanding language, such as dialects and colloquialisms. So, how can a computer that “thinks” in binary be programmed, line by line, to become fluent in any language? The answer is; it can’t. But, thanks to the advent of artificial intelligence (AI), a computer can now learn how to understand a language.

What Is Natural Language Processing?

Natural language processing (NLP) is a branch of AI. NLP relates to humans and computers communicating using natural language. NLP includes both speech recognition and reading text. Using machine learning, a computer is now able to learn how to understand our speech and writing. Computers can now look at more than the keywords to decipher our language. It can pick up on the more subtle aspects of our language to interpret the contextual meaning of the words.

Why is Natural Language Processing So Important?

In the past, computers could only work with structured languages. The language had to be precise and unambiguous. To program a computer to perform any task, you had to give it clear instructions. You could only use the limited number of commands that the computer understood. The syntax had to be perfect as well.

Even an end-user of a computer program needs to give the computer precise commands. Those who are old enough will remember that to use a PC you once had to know the common MS-DOS commands. That barrier was overcome, to a degree, with graphical user interfaces, such as Windows. Now, we can point to a file with a mouse, instead of having to know the name of the file.

NLP promises to remove the need for being so precise. Instead of having to learn the computer’s language, the computer will learn how to understand ours. A very basic application of NLP will be how we interface with computers. We won’t have to tell the computer to open our “aprilcashflowforcast.XLS” file. We will be able to ask the computer less precise questions, such as “How much cash have we got coming in this month?”

Practical Business Applications of Natural Language Processing

NLP is not an emerging technology that will, one day, have applications in business. It is a technology that is in use now. NLP is being used in applications such as online searching, and grammar checkers. That’s why you can now search on Google using normal sentences. NLP goes far beyond simplifying the computer/human interface, though. Being able to understand human language has many other practical applications. Here are few examples of how NLP is being used today:

Language Translation

NLP programs learn a language in the same way that humans do. And, like humans, if a machine can learn one language, it can learn many. There are now neural machine translation programs that can translate between languages. The first of these was Microsoft’s Bing Translator.

Chatbots

NLP has made chatbots far more effective. This has increased the applications that chatbots are now used for. In HR applications, for example, chatbots are now answering employees’ questions. There is a chatbot called Talla that will answer questions such as “Do I have any vacation left?”.

Document Reading Tools

NLP is also able to read and interpret the written word. One of the practical uses for this technology is the sifting of job applicants’ resumes. Machine learning allows text reading applications to learn synonyms. This is important when reading a resume because people use different terms to describe their personal qualities and their work history.

Sentiment Analysis

As NLP can understand the nuances of language, it can also understand the sentiment of the words. There is a technology known as opinion mining. This can analyze the opinion that people have of a brand by looking at blogs and social media profiles. It can understand the sentiment of posts and comments left by customers. Analyzing vast amounts of data like this would be an impossible task for a human.

Conclusion

Natural language processing is a major leap forward in AI technology. It removes the communication barrier that has always existed between machines and humans. The potential for the application of NLP in business is immense. A computer could now answer customer queries and take orders. Even if the customer uses obscure language. NLP is likely to remove the need for input devices, such as the keyboard and mouse as well. NLP matters, because it is about to revolutionize the way that we communicate with machines, and how they communicate with us.

Robotic Process Automation and Its Applications

Introduction

Many large enterprises are using robotic process automation (RPA) to reduce costs and improve efficiency. By implementing RPA, businesses can automate repetitive and mundane tasks. RPA could represent the first step towards true intelligent automation. But what is RPA, and what are its applications in business?

What Is Robotic Process Automation?

RPA is a term that can be applied to any computer program that automatically performs a repetitive function. In its simplest form, RPA is the automatic out-of-office message that your email software sends. More sophisticated RPA bots can log into an application, perform tasks, and log out again. RPA is not a part of an organization’s IT infrastructure. RPA sits on top of the infrastructure and automates tasks that humans would otherwise perform.

There are three main types of RPA bots. There are programmable bots that interact with other systems. There are intelligent RPA bots that can make decisions based on unstructured data. And, there are self-learning bots, such as chatbots.

Robotic Process Automation is a software technology that automates the execution of tasks, typically those that are repetitive and mundane.

Robotic Process Automation (RPA) is an emerging trend in business process automation. It is a type of computer programming that uses software to control the execution of routine tasks. RPA automates these routine tasks by mimicking human actions, such as clicking or filling out forms on a screen.

Examples of RPA Applications

RPA is suitable for use on tasks that are repetitive, well-documented, and well-defined. If the task is rule-based, and it does not alter often, then it is a task that could be completed by RPA. Robotic process automation can automate a wide variety of tasks in many different industries. Here are a few of the practical applications of RPA.

Web site scraping

RPA can be used to gather information from web pages. Examples of this include extracting and summarizing data from stock trading websites. Once the data has been collected and summarized, it can then be passed to humans for further analysis.

Automated email processing

Many organizations receive lots of emails asking the same questions. RPA can take care of some of these emails and respond with standard replies. The emails that the RPA bot cannot answer can then be forwarded to the appropriate personnel for answering.

Data Cleansing

Data cleansing is a good example of where RPA can be used to complete time-consuming tasks. If there are clear rules as to what constitutes bad data, RPA can filter out that bad data much more efficiently than humans.

Data Entry

One of the most far-reaching applications of RPA is that of data entry. An RPA bot can read original forms using optical character recognition (OCR) and then “key” the data into an application. This would be faster than a human could key the data, and it would be more accurate.

The Benefits of Robotic Process Automation

The benefits of RPA to businesses are many. RPA removes the human error factor from many tasks. The effectiveness of RPA can be limited by the accuracy of technologies, though. Technologies such as optical character recognition and speech recognition software. Even so, RPA bots can work 24/7 without breaks, and they never get bored. Bots also only need training once, and they will never quit their job.

RPA technology brings more than only cost savings. It also improves the customer experience through faster processing of data and faster resolution of queries. For employees, it brings that removal of the boring, repetitive tasks, so that they can spend more time on the important aspects of a role. RPA will change the nature of some jobs, so the HR aspects of those changes must be planned for as well. Overall, though, RPA will change the nature of employment for the better, rather than put people out of work.

Why Businesses Need to Consider Robotic Process Automation

RPA is a technology of today, not of the future. RPA bots are being used in accounting, finance, HR, and marketing now. American Express Global Business Travel uses RPA to automate the canceling of airline tickets and the issuing of refunds. At Walmart, RPA bots answer employees’ questions. In the future, some elements of almost every business process could be automated by RPA.

Robotic Process Automation won’t replace the need for humans. But it will remove the need for humans to perform programmable repetitive tasks. Businesses that do not embrace RPA will find themselves falling behind their competition. They will find that their customer service functions are slower and less efficient. And, they will become less competitive because of the additional costs of employing humans to perform tasks that are better performed by boots, and by not innovating by freeing up humans to do things that bots cannot. Eventually, businesses that do not use RPA will also find it difficult to employ people to perform repetitive tasks. Like their customers, their employees will have all moved to the companies that are using Robotic Process Automation to make life better for customers and employees.