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.

The Complete Guide to Machine Learning for Business Leaders

Machine Learning For Business Leaders

Machine Learning has garnered a significant share of recent press coverage in both tech and main street media. It is inextricably intertwined with, and central to, discussion and dialogue on topics ranging from big data in general to Facebook’s threat to privacy, Boston Dynamics creepy robotics, and Google’s exploitation of artificial intelligence for good and ill. As such, it is easy to view machine learning as either sinister or magical — neither of which is true. For today’s business leader, an objective and actionable understanding of machine learning are as important as an actionable understanding of finance and financial management.

Machine Learning is a subset of artificial intelligence, where computer algorithms are able to learn from data and modify their behavior accordingly. These algorithms have the ability to identify patterns in data that humans can’t see. Some commercial applications of Machine Learning include speech recognition, facial recognition, self-driving cars, and automatic stock trading.

Machine Learning has been around for decades, but it has only now begun to find its way into businesses. Recently, the number of organizations that are using machine learning has increased tremendously. As a result, there may be some misconceptions about machine learning that need to be clarified.

Machine learning is not just for scientists and computer programmers anymore. The use of machine learning can be applied to all industries and functional areas within an organization. Machine learning can help with customer retention, risk management, fraud detection, inventory management, product innovation, and many other areas.

What machine learning is

Machine learning (ML) is a data-driven system development paradigm. ML systems leverage data models, data analysis, and feedback to define and refine algorithms to improve model accuracy and system results.

ML systems work by analyzing data to detect patterns or by applying predefined rules to:

• Categorize or catalog like objects
• Predict likely outcomes or actions based on identified patterns
• Identify unknown patterns and relationships
• Detect anomalous or unexpected behaviors

Different algorithms learn in different ways. But in general, as new data are provided to the ML system the system “learns” and the algorithm’s performance improves over time.

Problems suited to machine learning

ML, like other software development paradigms, is not one-size-fits-all — some approaches are better suited to particular classes of problems and not suitable for others.

Machine learning is particularly suited to problems where:

• Logical rules are unavailable or insufficient to describe the environment — but actionable rules can be intuited
• Next actions are varied and the best action depends on conditions that cannot be identified in advance
• Understanding why an outcome is suggested is not as important as the accuracy of the outcome
• The data is problematic for traditional analytic methods

Now that you know what machine learning is and how to identify problems that lend themselves to ML solutions, let’s explore the steps to define and conduct an ML project.

How to plan and execute a machine learning project

Well executed ML systems follow these recommended steps:

1. Define Problem
2. Prepare Data
3. Evaluate Algorithms
4. Improve Results
5. Present Results

These steps, while seemingly generic and common to traditional software system development, require the perspective and attention gained from experience with ML system development.

The best way to approach machine learning system development is to work through an ML project end-to-end and cover the key steps with an experienced guide or team. Every step, from loading data, summarizing data, evaluating algorithms, making initial predictions, refining, and presenting results is improved by experience — much like an ML system.

Accordingly, your first project should be viewed as a learning process to understand the mechanics of machine learning, calibrate your expectations and provide a perspective for setting expectations, interpreting, and presenting results from dynamic, learning systems. After tackling your first project with the expert assistance you will be prepared to spot and sponsor the next, more consequential machine learning opportunity.

View the next post in this series: Docker Containers – What Business Leaders Need to Know