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.

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New OPIT Degrees launching (soon)!
Riccardo Ocleppo
Riccardo Ocleppo
Nov 29, 2023 2 min read


Soon, we will be launching four new Degrees for AY24-25 at OPIT – Open Institute of Technology
I want to offer a behind-the-scenes look at the Product Definition process that has shaped these upcoming programs.

🚀 Phase 1: Discovery (Late May – End of July)

Our journey began with intensive brainstorming sessions with OPIT’s Academic Board (Francesco Profumo, Lorenzo Livi, Alexiei Dingli, Andrea Pescino, Rosario Maccarrone) . We also conducted 50+ interviews with tech and digital entrepreneurs (both from startups and established firms), academics and students. Finally, we deep-dived into the “Future of Jobs 2023” report by the World Economic Forum and other valuable research.

🔍 Phase 2: Selection – Crafting Our Roadmap (July – August)

Our focus? Introducing new degrees addressing critical workforce shortages and upskilling/reskilling needs for the next 5-10 years, promising significant societal impact and a broad market reach.
Our decision? To channel our energies on full BScs and MScs, and steer away from shorter courses or corporate-focused offerings. This aligns perfectly with our core mission.

💡 Focus Areas Unveiled!

We’re thrilled to concentrate on pivotal fields like:

  • Cybersecurity
  • Advanced AI
  • Digital Business
  • Metaverse & Gaming
  • Cloud Computing (less “glamorous”, but market demand is undeniable).

🎓 Phase 3: Definition – Shaping the Degrees (August – November)

With an expert in each of the above fields, and with the strong collaboration of our Academic Director, Prof. Lorenzo Livi , we embarked on a rigorous “drill-down process”. Our goal? To meld modern theoretical knowledge with cutting-edge competencies and skills. This phase included interviewing over 60+ top academics, industry professionals, and students and get valuable, program-specific, insights from our Marketing department.

🌟 Phase 4: Accreditation and Launch – The Final Stretch

We’re currently in the accreditation process, gearing up for the launch. The focus is now shifting towards marketing, working closely with Greta Maiocchi and her Marketing and Admissions team. Together, we’re translating our new academic offering into a compelling value proposition for the market.

Stay tuned for more updates!

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Five Reasons to Study Online at OPIT
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Nov 29, 2023 9 min read

Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.

The demand for these types of courses clearly exists.

In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.

Enter the Open Institute of Technology (OPIT).

Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?

Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.

Reason 1 – A Practical Approach

OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.

“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.

OPIT takes a different approach.

It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.

An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.

Reason 2 – Flexibility Combined With Support

Flexibility in how you study is one of the main benefits of any online course.

You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.

Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.

Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.

In this focus on support, OPIT showcases one of its main differences from other online platforms.

You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.

Reason 3 – OPIT Can Adapt to Change Quickly

The field of computer science is constantly evolving.

In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.

Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.

“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.

That left Ocleppo feeling frustrated.

When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.

This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.

Reason 4 – OPIT Delivers Tier One Education

Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.

“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.

OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.

It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.

Take its Bachelor’s degree in computer science as an example.

Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.

Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.

Reason 5 – The Learning Experience

Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.

After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.

Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.

OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.

Choose OPIT for Your Computer Science Education

At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.

There’s also more to come.

Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.

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