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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Il Sole 24 Ore: 100 thousand IT professionals missing
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 14, 2024 6 min read

Written on April 24th 2024

Source here: Il Sole 24 Ore (full article in Italian)

Open Institute of Technology: 100 thousand IT professionals missing

Eurostat data processed and disseminated by OPIT. Stem disciplines: the share of graduates in Italy between the ages of 20 and 29 is 18.3%, compared to the European 21.9%

Today, only 29% of young Italians between 25 and 34 have a degree. Not only that: compared to other European countries, the comparison is unequal given that the average in the Old Continent is 46%, bringing Italy to the penultimate place in this ranking, ahead only of Romania. The gap is evident even if the comparison is limited to STEM disciplines (science, technology, engineering and mathematics) where the share of graduates in Italy between the ages of 20 and 29 is 18.3%, compared to the European 21.9%, with peaks of virtuosity which in the case of France that reaches 29.2%. Added to this is the continuing problem of the mismatch between job supply and demand, so much so that 62.8% of companies struggle to find professionals in the technological and IT fields.

The data

The Eurostat data was processed and disseminated by OPIT – Open Institute of Technology. an academic institution accredited at European level, active in the university level education market with online Bachelor’s and Master’s degrees in the technological and digital fields. We are therefore witnessing a phenomenon with worrying implications on the future of the job market in Italy and on the potential loss of competitiveness of our companies at a global level, especially if inserted in a context in which the macroeconomic scenario in the coming years will undergo a profound discontinuity linked to the arrival of “exponential” technologies such as Artificial Intelligence and robotics, but also to the growing threats related to cybersecurity.

Requirements and updates

According to European House Ambrosetti, over 2,000,000 professionals will have to update their skills in the Digital and IT area by 2026, also to take advantage of the current 100,000 vacant IT positions, as estimated by Frank Recruitment Group. But not only that: the Italian context, which is unfavorable for providing the job market with graduates and skills, also has its roots in the chronic birth rate that characterizes our country: according to ISTAT data, in recent years the number of newborns has fallen by 28%, bringing Italy’s birth rate to 1.24, among the lowest in Europe, where the average is 1.46.

Profumo: “Structural deficiency”

“The chronic problem of the absence of IT professionals is structural and of a dual nature: on one hand the number of newborns – therefore, potential “professionals of the future” – is constantly decreasing; on the other hand, the percentage of young people who acquires degrees are firmly among the lowest in Europe”, declared Francesco Profumo, former Minister of Education and rector of OPIT – Open Institute of Technology. “The reasons are varied: from the cost of education (especially if undertaken off-site), to a university offering that is poorly aligned with changes in society, to a lack of awareness and orientation towards STEM subjects, which guarantee the highest employment rates. Change necessarily involves strong investments in the university system (and, in general, in the education system) at the level of the country, starting from the awareness that a functioning education system is the main driver of growth and development in the medium to long term. It is a debated and discussed topic on which, however, a clear and ambitious position is never taken.”

Stagnant context and educational offer

In this stagnant context, the educational offer that comes from online universities increasingly meets the needs of flexibility, quality and cost of recently graduated students, university students looking for specialization and workers interested in updating themselves with innovative skills. According to data from the Ministry of University and Research, enrollments in accredited online universities in Italy have grown by over 141 thousand units in ten years (since 2011), equal to 293.9%. Added to these are the academic institutions accredited at European level, such as OPIT, whose educational offering is overall capable of opening the doors to hundreds of thousands of students, with affordable costs and extremely innovative and updated degree paths.

Analyzing the figures

An analysis of Eurostat statistics relating to the year 2021 highlights that 27% of Europeans aged between 16 and 74 have attended an entirely digital course. The highest share is recorded in Ireland (46%), Finland and Sweden (45%) and the Netherlands (44%). The lowest in Romania (10%), Bulgaria (12%) and Croatia (18%). Italy is at 20%. “With OPIT” – adds Riccardo Ocleppo, founder and director – “we have created a new model of online academic institution, oriented towards new technologies, with innovative programs, a strong practical focus, and an international approach, with professors and students from 38 countries around the world, and teaching in English. We intend to train Italian students not only on current and updated skills, but to prepare them for an increasingly dynamic and global job market. Our young people must be able to face the challenges of the future like those who study at Stanford or Oxford: with solid skills, but also with relational and attitudinal skills that lead them to create global companies and startups or work in multinationals like their international colleagues. The increasing online teaching offer, if well structured and with quality, represents an incredible form of democratization of education, making it accessible at low costs and with methods that adapt to the flexibility needs of many working students.”

Point of reference

With two degrees already starting in September 2023 – a three-year degree (BSc) in Modern Computer Science and a specialization (MSc) in Applied Data Science & AI – and 4 starting in September 2024: a three-year degree (BSc) in Digital Business, and the specializations (MSc) in Enterprise Cybersecurity, Applied Digital Business and Responsible Artificial Intelligence (AI), OPIT is an academic institution of reference for those who intend to respond to the demands of a job market increasingly oriented towards the field of artificial intelligence. Added to this are a high-profile international teaching staff and an exclusively online educational offer focused on the technological and digital fields.

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Times of India: The 600,000 IT job shortage in India and how to solve it
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 2, 2024 3 min read

Written on April 25th 2024

Source here: Times of India 

The job market has never been a straightforward path. Ask anyone who has ever looked for a job, certainly within the last decade, and they can tell you as much. But with the rapid development of AI and machine learning, concerns are growing for people about their career options, with a report from Randstad finding that 7 in 10 people in India are concerned about their job being eliminated by AI.

 Employers have their own share of concerns. According to The World Economic Forum, 97 million new AI-related jobs will be created by 2025 and the share of jobs requiring AI skills will increase by 58%. The IT industry in India is experiencing a tremendous surge in demand for skilled professionals on disruptive technologies like artificial intelligence, machine learning, blockchain, cybersecurity and, according to Nasscom, this is leading to a shortage of 600,000 profiles.

 So how do we fill those gaps? Can we democratize access to top-tier higher education in technology?

These are the questions that Riccardo Ocleppo, the engineer who founded a hugely successful ed-tech platform connecting international students with global Universities, Docsity, asked himself for years. Until he took action and launched the Open Institute of Technology (OPIT), together with the Former Minister of Education of Italy, Prof. Francesco Profumo, to help people take control of their future careers.

OPIT offers BSc and MSc degrees in Computer Science, AI, Data Science, Cybersecurity, and Digital Business, attracting students from over 38 countries worldwide. Through innovative learning experiences and affordable tuition fees starting at €4,050 per year, OPIT empowers students to pursue their educational goals without the financial and personal burden of relocating.

The curriculum, delivered through a mix of live and pre-recorded lectures, equips students with the latest technology skills, as well as business and strategic acumen necessary for careers in their chosen fields. Moreover, OPIT’s EU-accredited degrees enable graduates to pursue employment opportunities in Europe, with recognition by WES facilitating transferability to the US and Canada.

OPIT’s commitment to student success extends beyond academics, with a full-fledged career services department led by Mike McCulloch. Remote students benefit from OPIT’s “digital campus,” fostering connections through vibrant discussion forums, online events, and networking opportunities with leading experts and professors.

Faculty at OPIT, hailing from prestigious institutions and industry giants like Amazon and Microsoft, bring a wealth of academic and practical experience to the table. With a hands-on, practical teaching approach, OPIT prepares students for the dynamic challenges of the modern job market.

In conclusion, OPIT stands as a beacon of hope for individuals seeking to future-proof their careers in technology. By democratizing access to high-quality education and fostering a global learning community, OPIT empowers students to seize control of their futures and thrive in the ever-evolving tech landscape.

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