Tens of thousands of businesses go under every year. There are various culprits, but one of the most common causes is the inability of companies to streamline their customer experience. Many technologies have emerged to save the day, one of which is natural language processing (NLP).

But what is natural language processing? In simple terms, it’s the capacity of computers and other machines to understand and synthesize human language.

It may already seem like it would be important in the business world and trust us – it is. Enterprises rely on this sophisticated technology to facilitate different language-related tasks. Plus, it enables machines to read and listen to language as well as interact with it in many other ways.

The applications of NLP are practically endless. It can translate and summarize texts, retrieve information in a heartbeat, and help set up virtual assistants, among other things.

Looking to learn more about these applications? You’ve come to the right place. Besides use cases, this introduction to natural language processing will cover the history, components, techniques, and challenges of NLP.

History of Natural Language Processing

Before getting to the nuts and bolts of NLP basics, this introduction to NLP will first examine how the technology has grown over the years.

Early Developments in NLP

Some people revolutionized our lives in many ways. For example, Alan Turing is credited with several groundbreaking advancements in mathematics. But did you also know he paved the way for modern computer science, and by extension, natural language processing?

In the 1950s, Turing wanted to learn if humans could talk to machines via teleprompter without noticing a major difference. If they could, he concluded the machine would be capable of thinking and speaking.

Turin’s proposal has since been used to gauge this ability of computers and is known as the Turing Test.

Evolution of NLP Techniques and Algorithms

Since Alan Turing set the stage for natural language processing, many masterminds and organizations have built upon his research:

• 1958 – John McCarthy launched his Locator/Identifier Separation Protocol.
• 1964 – Joseph Wizenbaum came up with a natural language processing model called ELIZA.
• 1980s – IBM developed an array of NLP-based statistical solutions.
• 1990s – Recurrent neural networks took center stage.

The Role of Artificial Intelligence and Machine Learning in NLP

Discussing NLP without mentioning artificial intelligence and machine learning is like leaving a glass half empty. So, what’s the role of these technologies in NLP? It’s pivotal, to say the least.

AI and machine learning are the cornerstone of most NLP applications. They’re the engine of the NLP features that produce text, allowing NLP apps to turn raw data into usable information.

Key Components of Natural Language Processing

The phrase building blocks get thrown around a lot in the computer science realm. It’s key to understanding different parts of this sphere, including natural language processing. So, without further ado, let’s rifle through the building blocks of NLP.

Syntax Analysis

An NLP tool without syntax analysis would be lost in translation. It’s a paramount stage since this is where the program extracts meaning from the provided information. In simple terms, the system learns what makes sense and what doesn’t. For instance, it rejects contradictory pieces of data close together, such as “cold Sun.”

Semantic Analysis

Understanding someone who jumbles up words is difficult or impossible altogether. NLP tools recognize this problem, which is why they undergo in-depth semantic analysis. The network hits the books, learning proper grammatical structures and word orders. It also determines how to connect individual words and phrases.

Pragmatic Analysis

A machine that relies only on syntax and semantic analysis would be too machine-like, which goes against Turing’s principles. Salvation comes in the form of pragmatic analysis. The NLP software uses knowledge outside the source (e.g., textbook or paper) to determine what the speaker actually wants to say.

Discourse Analysis

When talking to someone, there’s a point to your conversation. An NLP system is just like that, but it needs to go through extensive training to achieve the same level of discourse. That’s where discourse analysis comes in. It instructs the machine to use a coherent group of sentences that have a similar or the same theme.

Speech Recognition and Generation

Once all the above elements are perfected, it’s blast-off time. The NLP has everything it needs to recognize and generate speech. This is where the real magic happens – the system interacts with the user and starts using the same language. If each stage has been performed correctly, there should be no significant differences between real speech and NLP-based applications.

Natural Language Processing Techniques

Different analyses are common for most (if not all) NLP solutions. They all point in one direction, which is recognizing and generating speech. But just like Google Maps, the system can choose different routes. In this case, the routes are known as NLP techniques.

Rule-Based Approaches

Rule-based approaches might be the easiest NLP technique to understand. You feed your rules into the system, and the NLP tool synthesizes language based on them. If input data isn’t associated with any rule, it doesn’t recognize the information – simple as that.

Statistical Methods

If you go one level up on the complexity scale, you’ll see statistical NLP methods. They’re based on advanced calculations, which enable an NLP platform to predict data based on previous information.

Neural Networks and Deep Learning

You might be thinking: “Neural networks? That sounds like something out of a medical textbook.” Although that’s not quite correct, you’re on the right track. Neural networks are NLP techniques that feature interconnected nodes, imitating neural connections in your brain.

Deep learning is a sub-type of these networks. Basically, any neural network with at least three layers is considered a deep learning environment.

Transfer Learning and Pre-Trained Language Models

The internet is like a massive department store – you can find almost anything that comes to mind here. The list includes pre-trained language models. These models are trained on enormous quantities of data, eliminating the need for you to train them using your own information.

Transfer learning draws on this concept. By tweaking pre-trained models to accommodate a particular project, you perform a transfer learning maneuver.

Applications of Natural Language Processing

With so many cutting-edge processes underpinning NLP, it’s no surprise it has practically endless applications. Here are some of the most common natural language processing examples:

• Search engines and information retrieval – An NLP-based search engine understands your search intent to retrieve accurate information fast.
• Sentiment analysis and social media monitoring – NLP systems can even determine your emotional motivation and uncover the sentiment behind social media content.
• Machine translation and language understanding – NLP software is the go-to solution for fast translations and understanding complex languages to improve communication.
• Chatbots and virtual assistants – A state-of-the-art NLP environment is behind most chatbots and virtual assistants, which allows organizations to enhance customer support and other key segments.
• Text summarization and generation – A robust NLP infrastructure not only understands texts but also summarizes and generates texts of its own based on your input.

Challenges and Limitations of Natural Language Processing

Natural language processing in AI and machine learning is mighty but not almighty. There are setbacks to this technology, but given the speedy development of AI, they can be considered a mere speed bump for the time being:

• Ambiguity and complexity of human language – Human language keeps evolving, resulting in ambiguous structures NLP often struggles to grasp.
• Cultural and contextual nuances – With approximately 4,000 distinct cultures on the globe, it’s hard for an NLP system to understand the nuances of each.
• Data privacy and ethical concerns – As every NLP platform requires vast data, the methods for sourcing this data tend to trigger ethical concerns.
• Computational resources and computing power – The more polished an NLP tool becomes, the greater the computing power must be, which can be hard to achieve.

The Future of Natural Language Processing

The final part of our take on natural language processing in artificial intelligence asks a crucial question: What does the future hold for NLP?

• Advancements in artificial intelligence and machine learning – Will AI and machine learning advancements help NLP understand more complex and nuanced languages faster?
• Integration of NLP with other technologies – How well will NLP integrate with other technologies to facilitate personal and corporate use?
• Personalized and adaptive language models – Can you expect developers to come up with personalized and adaptive language models to accommodate those with speech disorders better?
• Ethical considerations and guidelines for NLP development – How will the spearheads of NLP development address ethical problems if the technology requires more and more data to execute?

The Potential of Natural Language Processing Is Unrivaled

It’s hard to find a technology that’s more important for today’s businesses and society as a whole than natural language processing. It streamlines communication, enabling people from all over the world to connect with each other.

The impact of NLP will amplify if the developers of this technology can address the above risks. By honing the software with other platforms while minimizing privacy issues, they can dispel any concerns associated with it.

If you want to learn more about NLP, don’t stop here. Use these natural language processing notes as a stepping stone for in-depth research. Also, consider an NLP course to gain a deep understanding of this topic.

The human brain is among the most complicated organs and one of nature’s most amazing creations. The brain’s capacity is considered limitless; there isn’t a thing it can’t remember. Although many often don’t think about it, the processes that happen in the mind are fascinating.

As technology evolved over the years, scientists figured out a way to make machines think like humans, and this process is called machine learning. Like cars need fuel to operate, machines need data and algorithms. With the application of adequate techniques, machines can learn from this data and even improve their accuracy as time passes.

Two basic machine learning approaches are supervised and unsupervised learning. You can already assume the biggest difference between them based on their names. With supervised learning, you have a “teacher” who shows the machine how to analyze specific data. Unsupervised learning is completely independent, meaning there are no teachers or guides.

This article will talk more about supervised and unsupervised learning, outline their differences, and introduce examples.

Supervised Learning

Imagine a teacher trying to teach their young students to write the letter “A.” The teacher will first set an example by writing the letter on the board, and the students will follow. After some time, the students will be able to write the letter without assistance.

Supervised machine learning is very similar to this situation. In this case, you (the teacher) train the machine using labeled data. Such data already contains the right answer to a particular situation. The machine then uses this training data to learn a pattern and applies it to all new datasets.

Note that the role of a teacher is essential. The provided labeled datasets are the foundation of the machine’s learning process. If you withhold these datasets or don’t label them correctly, you won’t get any (relevant) results.

Supervised learning is complex, but we can understand it through a simple real-life example.

Suppose you have a basket filled with red apples, strawberries, and pears and want to train a machine to identify these fruits. You’ll teach the machine the basic characteristics of each fruit found in the basket, focusing on the color, size, shape, and other relevant features. If you introduce a “new” strawberry to the basket, the machine will analyze its appearance and label it as “strawberry” based on the knowledge it acquired during training.

Types of Supervised Learning

You can divide supervised learning into two types:

• Classification – You can train machines to classify data into categories based on different characteristics. The fruit basket example is the perfect representation of this scenario.
• Regression – You can train machines to use specific data to make future predictions and identify trends.

Supervised Learning Algorithms

Supervised learning uses different algorithms to function:

• Linear regression – It identifies a linear relationship between an independent and a dependent variable.
• Logistic regression – It typically predicts binary outcomes (yes/no, true/false) and is important for classification purposes.
• Support vector machines – They use high-dimensional features to map data that can’t be separated by a linear line.
• Decision trees – They predict outcomes and classify data using tree-like structures.
• Random forests – They analyze several decision trees to come up with a unique prediction/result.
• Neural networks – They process data in a unique way, very similar to the human brain.

Supervised Learning: Examples and Applications

There’s no better way to understand supervised learning than through examples. Let’s dive into the real estate world.

Suppose you’re a real estate agent and need to predict the prices of different properties in your city. The first thing you’ll need to do is feed your machine existing data about available houses in the area. Factors like square footage, amenities, a backyard/garden, the number of rooms, and available furniture, are all relevant factors. Then, you need to “teach” the machine the prices of different properties. The more, the better.

A large dataset will help your machine pick up on seemingly minor but significant trends affecting the price. Once your machine processes this data and you introduce a new property to it, it will be able to cross-reference its features with the existing database and come up with an accurate price prediction.

The applications of supervised learning are vast. Here are the most popular ones:

• Sales – Predicting customers’ purchasing behavior and trends
• Finance – Predicting stock market fluctuations, price changes, expenses, etc.
• Healthcare – Predicting risk of diseases and infections, surgery outcomes, necessary medications, etc.
• Weather forecasts – Predicting temperature, humidity, atmospheric pressure, wind speed, etc.
• Face recognition – Identifying people in photos

Unsupervised Learning

Imagine a family with a baby and a dog. The dog lives inside the house, so the baby is used to it and expresses positive emotions toward it. A month later, a friend comes to visit, and they bring their dog. The baby hasn’t seen the dog before, but she starts smiling as soon as she sees it.

Why?

Because the baby was able to draw her own conclusions based on the new dog’s appearance: two ears, tail, nose, tongue sticking out, and maybe even a specific noise (barking). Since the baby has positive emotions toward the house dog, she also reacts positively to a new, unknown dog.

This is a real-life example of unsupervised learning. Nobody taught the baby about dogs, but she still managed to make accurate conclusions.

With supervised machine learning, you have a teacher who trains the machine. This isn’t the case with unsupervised learning. Here, it’s necessary to give the machine freedom to explore and discover information. Therefore, this machine learning approach deals with unlabeled data.

Types of Unsupervised Learning

There are two types of unsupervised learning:

• Clustering – Grouping uncategorized data based on their common features.
• Dimensionality reduction – Reducing the number of variables, features, or columns to capture the essence of the available information.

Unsupervised Learning Algorithms

Unsupervised learning relies on these algorithms:

• K-means clustering – It identifies similar features and groups them into clusters.
• Hierarchical clustering – It identifies similarities and differences between data and groups them hierarchically.
• Principal component analysis (PCA) – It reduces data dimensionality while boosting interpretability.
• Independent component analysis (ICA) – It separates independent sources from mixed signals.
• T-distributed stochastic neighbor embedding (t-SNE) – It explores and visualizes high-dimensional data.

Unsupervised Learning: Examples and Applications

Let’s see how unsupervised learning is used in customer segmentation.

Suppose you work for a company that wants to learn more about its customers to build more effective marketing campaigns and sell more products. You can use unsupervised machine learning to analyze characteristics like gender, age, education, location, and income. This approach is able to discover who purchases your products more often. After getting the results, you can come up with strategies to push the product more.

Unsupervised learning is often used in the same industries as supervised learning but with different purposes. For example, both approaches are used in sales. Supervised learning can accurately predict prices relying on past data. On the other hand, unsupervised learning analyzes the customers’ behaviors. The combination of the two approaches results in a quality marketing strategy that can attract more buyers and boost sales.

Another example is traffic. Supervised learning can provide an ETA to a destination, while unsupervised learning digs a bit deeper and often looks at the bigger picture. It can analyze a specific area to pinpoint accident-prone locations.

Differences Between Supervised and Unsupervised Learning

These are the crucial differences between the two machine learning approaches:

• Data labeling – Supervised learning uses labeled datasets, while unsupervised learning uses unlabeled, “raw” data. In other words, the former requires training, while the latter works independently to discover information.
• Algorithm complexity – Unsupervised learning requires more complex algorithms and powerful tools that can handle vast amounts of data. This is both a drawback and an advantage. Since it operates on complex algorithms, it’s capable of handling larger, more complicated datasets, which isn’t a characteristic of supervised learning.
• Use cases and applications – The two approaches can be used in the same industries but with different purposes. For example, supervised learning is used in predicting prices, while unsupervised learning is used in detecting customers’ behavior or anomalies.
• Evaluation metrics – Supervised learning tends to be more accurate (at least for now). Machines still require a bit of our input to display accurate results.

Choose Wisely

Do you need to teach your machine different data, or can you trust it to handle the analysis on its own? Think about what you want to analyze. Unsupervised and supervised learning may sound similar, but they have different uses. Choosing an inadequate approach leads to unreliable, irrelevant results.

Supervised learning is still more popular than unsupervised learning because it offers more accurate results. However, this approach can’t handle larger, complex datasets and requires human intervention, which isn’t the case with unsupervised learning. Therefore, we may see a rise in the popularity of the unsupervised approach, especially as the technology evolves and enables more accuracy.

The term “big data” is self-explanatory: it’s a large collection of data. However, to be classified as “big,” data needs to meet specific criteria. Big data is huge in volume, gets even bigger over time, arrives with ever-higher velocity, and is so complex that no traditional tools can handle it.

Big data analytics is the (complex) process of analyzing these huge chunks of data to discover different information. The process is especially important for small companies that use the uncovered information to design marketing strategies, conduct market research, and follow the latest industry trends.

In this introduction to big data analytics, we’ll dig deep into big data and uncover ways to analyze it. We’ll also explore its (relatively short) history and evolution and present its advantages and drawbacks.

History and Evolution of Big Data

We’ll start this introduction to big data with a short history lesson. After all, we can’t fully answer the “what is big data?” question if we don’t know its origins.

Let’s turn on our time machine and go back to the 1960s. That’s when the first major change that marked the beginning of the big data era took place. The advanced development of data centers, databases, and innovative processing methods facilitated the rise of big data.

Relational databases (storing and offering access to interconnected data points) have become increasingly popular. While people had ways to store data much earlier, experts consider that this decade set the foundations for the development of big data.

The next major milestone was the emergence of the internet and the exponential growth of data. This incredible invention made handling and analyzing large chunks of information possible. As the internet developed, big data technologies and tools became more advanced.

This leads us to the final destination of short time travel: the development of big data analytics, i.e., processes that allow us to “digest” big data. Since we’re witnessing exceptional technological developments, the big data journey is yet to continue. We can only expect the industry to advance further and offer more options.

Big Data Technologies and Tools

What tools and technologies are used to decipher big data and offer value?

Data Storage and Management

Data storage and management tools are like virtual warehouses where you can pack up your big data safely and work with it as needed. These tools feature a powerful infrastructure that lets you access and fetch the desired information quickly and easily.

Data Processing and Analytics Framework

Processing and analyzing huge amounts of data are no walk in the park. But they can be, thanks to specific tools and technologies. These valuable allies can clean and transform large piles of information into data you can use to pursue your goals.

Machine Learning and Artificial Intelligence Platforms

Machine learning and artificial intelligence platforms “eat” big data and perform a wide array of functions based on the discoveries. These technologies can come in handy with testing hypotheses and making important decisions. Best of all, they require minimal human input; you can relax while AI works its magic.

Data Visualization Tools

Making sense of large amounts of data and presenting it to investors, stakeholders, and team members can feel like a nightmare. Fortunately, you can turn this nightmare into a dream come true with big data visualization tools. Thanks to the tools, creating stunning graphs, dashboards, charts, and tables and impressing your coworkers and superiors has never been easier.

Big Data Analytics Techniques and Methods

What techniques and methods are used in big data analytics? Let’s find the answer.

Descriptive Analytics

Descriptive analytics is like a magic wand that turns raw data into something people can read and understand. Whether you want to generate reports, present data on a company’s revenue, or analyze social media metrics, descriptive analytics is the way to go.

It’s mostly used for:

• Data summarization and aggregation
• Data visualization

Diagnostic Analytics

Have a problem and want to get detailed insight into it? Diagnostic analytics can help. It identifies the root of an issue, helping you figure out your next move.

Some methods used in diagnostic analytics are:

• Data mining
• Root cause analysis

Predictive Analytics

Predictive analytics is like a psychic that looks into the future to predict different trends.

Predictive analytics often uses:

• Regression analysis
• Time series analysis

Prescriptive Analytics

Prescriptive analytics is an almighty problem-solver. It usually joins forces with descriptive and predictive analytics to offer an ideal solution to a particular problem.

Some methods prescriptive analytics uses are:

• Optimization techniques
• Simulation and modeling

Applications of Big Data Analytics

Big data analytics has found its home in many industries. It’s like the not-so-secret ingredient that can make the most of any niche and lead to desired results.

Business and Finance

How do business and finance benefit from big data analytics? These industries can flourish through better decision-making, investment planning, fraud detection and prevention, and customer segmentation and targeting.

Healthcare

Healthcare is another industry that benefits from big data analytics. In healthcare, big data is used to create patient databases, personal treatment plans, and electronic health records. This data also serves as an excellent foundation for accurate statistics about treatments, diseases, patient backgrounds, risk factors, etc.

Government and Public Sector

Big data analytics has an important role in government and the public sector. Analyzing different data improves efficiency in terms of costs, innovation, crime prediction and prevention, and workforce. Multiple government parts often need to work together to get the best results.

As technology advances, big data analytics has found another major use in the government and public sector: smart cities and infrastructure. With precise and thorough analysis, it’s possible to bring innovation and progress and implement the latest features and digital solutions.

Sports and Entertainment

Sports and entertainment are all about analyzing the past to predict the future and improve performance. Whether it’s analyzing players to create winning strategies or attracting the audience and freshening up the content, big data analytics is like a valuable player everyone wants on their team.

Challenges and Ethical Considerations in Big Data Analytics

Big data analytics represent doors to new worlds of information. But opening these doors often comes with certain challenges and ethical considerations.

Data Privacy and Security

One of the major challenges (and the reason some people aren’t fans of big data analytics) is data privacy and security. The mere fact that personal information can be used in big data analytics can make individuals feel exploited. Since data breaches and identity thefts are, unfortunately, becoming more common, it’s no surprise some people feel this way.

Fortunately, laws like GDPR and CCPA give individuals more control over the information others can collect from them.

Data Quality and Accuracy

Big data analytics can sometimes be a dead end. If the material wasn’t handled correctly, or the data was incomplete to start with, the results themselves won’t be adequate.

Algorithmic Bias and Fairness

Big data analytics is based on algorithms, which are designed by humans. Hence, it’s not unusual to assume that these algorithms can be biased (or unfair) due to human prejudices.

Ethical Use of Big Data Analytics

The ethical use of big data analytics concerns the “right” and “wrong” in terms of data usage. Can big data’s potential be exploited to the fullest without affecting people’s right to privacy?

Future Trends and Opportunities in Big Data Analytics

Although it has proven useful in many industries, big data analytics is still relatively young and unexplored.

Integration of Big Data Analytics With Emerging Technologies

It seems that new technologies appear in the blink of an eye. Our reality today (in a technological sense) looks much different than just two or three years ago. Big data analytics is now intertwined with emerging technologies that give it extra power, accuracy, and quality.

Cloud computing, advanced databases, the Internet of Things (IoT), and blockchain are only some of the technologies that shape big data analytics and turn it into a powerful giant.

Advancements in Machine Learning and Artificial Intelligence

Machines may not replace us (at least not yet), but it’s impossible to deny their potential in many industries, including big data analytics. Machine learning and artificial intelligence allow for analyzing huge amounts of data in a short timeframe.

Machines can “learn” from their own experience and use this knowledge to make more accurate predictions. They can pinpoint unique patterns in piles of information and estimate what will happen next.

New Applications and Industries Adopting Big Data Analytics

One of the best characteristics of big data analytics is its versatility and flexibility. Accordingly, many industries use big data analytics to improve their processes and achieve goals using reliable information.

Every day, big data analytics finds “new homes” in different branches and niches. From entertainment and medicine to gambling and architecture, it’s impossible to ignore the importance of big data and the insights it can offer.

These days, we recognize the rise of big data analytics in education (personalized learning) and agriculture (environmental monitoring).

Workforce Development and Education in Big Data Analytics

Analyzing big data is impossible without the workforce capable of “translating” the results and adopting emerging technologies. As big data analytics continues to develop, it’s vital not to forget about the cog in the wheel that holds everything together: trained personnel. As technology evolves, specialists need to continue their education (through training and certification programs) to stay current and reap the many benefits of big data analytics.

Turn Data to Your Advantage

Whatever industry you’re in, you probably have goals you want to achieve. Naturally, you want to achieve them as soon as possible and enjoy the best results. Instead of spending hours and hours going through piles of information, you can use big data analytics as a shortcut. Different types of big data technologies can help you improve efficiency, analyze risks, create targeted promotions, attract an audience, and, ultimately, increase revenue.

While big data offers many benefits, it’s also important to be aware of the potential risks, including privacy concerns and data quality.

Since the industry is changing (faster than many anticipated), you should stay informed and engaged if you want to enjoy its advantages.