Basics of Natural Language Processing

We don’t even notice all the ways Natural Language Processing is present in our daily lives. If you think about it, Natural Language Processing is present in:

  • Voice recognition in our smartphones
  • Translation of the pages in foreign languages
  • Customer Support Chatbots in eCommerce stores
  • Spam filters in our email inboxes
  • Report generation in our analytics tools

NLP is one of the integral elements of the business processes because it automates the interpretation of business intelligence and streamlines the operation.

In this introduction to Natural Language Processing, we will explain, what it is all about, how it works and its role in the modern world.

What is Natural Language Processing?

Natural Language Processing (aka NLP) is a field of computer science, Artificial Intelligence focused on the ability of the machines to comprehend language and interpret messages.

We can define NLP as a set of algorithms designed to explore, recognize, and utilize text-based information and identify insights for the benefit of the business operation.

As such, natural language processing and generation algorithms form a backbone for the majority of automated processes.

NLP gives the computer the skills to:

  • understand informally written queries;
  • extract the meaning out of it;
  • generate the responses of its own;
  • perform requested tasks.

The global task of NLP is streamlining the interaction between human operators and machines via more flexible conversational interfaces.

The NLP brings several value-added benefits to the table:

  • Insights into the content of the text (what is this text about?)
  • Exploration of the context of the message (Why, when, where, how it is about?)
  • Identification of the opportunities (facts, intents, sentiments) behind the message or “reading between the lines.”

The Origins of NLP technology

To continue our NLP introduction we should say about the roots of NLP technology, which go back into the times of the Cold War. The first practical application of Natural Language Processing was the translation of the messages from Russian to English to understand what the commies were at. The results were lackluster, but it was a step in the right direction. It took decades before the computers became powerful enough to handle NLP operations. You may check out current business applications of NLP in our article.

For a while, the major issue with NLP applications was flexibility. Long story short: early NLP software was stiff and not very practical. There was always something sore sticking out and breaking the game because language is complex and there is much going behind the words that were beyond the algorithm’s reach. Because of that, the algorithms required a lot of oversight and close attention to the details.

However, with the emergence of big data and machine learning algorithms, the task of fine-tuning and training Natural Language Processing models became less of an undertaking and more of a routine job.

How does natural language processing work?

In essence, Natural Language Processing is all about providing tools to enable the machine’s comprehension of language on a deeper level than straightforward commands.

This means the NLP models deal with a variety of different aspects of language, including:

  • Semantics – relations between words, sentences, paragraphs, and so on
  • Morphology – structure, and content of word forms
  • Phonology – a sound organization of words
  • Syntax – structural governance of the texts
  • Pragmatics – the way context contributes to meaning

The whole procedure involves the following steps:

  • The text is segmented into meaningful bits (topics, sentences, paragraphs, etc.)
  • Bag of words – counts words and their occurrences throughout the text.
  • After that, the words in the sentences are split apart. This process is called tokenization;
  • Then Parts of speech are tagged through the body of text
  • Term Frequency-Inverse Document Frequency (TF-IDF) – determines the importance of certain words in a corpus.
  • This process is continued with Named Entity Recognition which finds specific words that are names (people’s or company’s names, job titles, locations, product names, events, number figures, and others) or are related to them.
  • Next goes Stop Words Removal – this process removes the everyday language stuff like pronouns and prepositions. This process can be referred to as cleaning the text from irrelevant or noisy material. Stop words may also include anything deemed inconsequential for the particular use case.
  • The next step is Stemming – the process of separating the affixes from the words and extracting the root of the word. This includes prefixes (as in “biochemistry) and suffixes (as in “laughable”).
  • Then goes Lemmatization – the process of reducing the words to their base form and finding the variations of the word to form a distinct group. This includes the transformation of the words from one part of speech (as in the noun “walk” to the verb “walking”) to another or transformation from one time to another (from the present “write” to past “wrote”).
  • After that, the algorithm figures out how the words relate to each other. This process is called Dependency Parsing.
  • Topic modeling is applied to discover hidden structures or patterns in the text. The process involves text clustering into meaningful bits. The method also includes text chunking, which identifies the constituent parts of the sentence and the relations between the elements

That’s how an algorithm is capable of comprehending the text.

Because of the sheer volume of the information to be processed – NLP involves a combination of supervised and unsupervised machine learning algorithms. At first, the process involves clustering – exploring the texts and their content, then the procedure involves classification – sorting out the specific elements.

The models are trained on datasets (known as corpora) that include a lot of different examples of language use related to the use case requirements. The analysis of the text creates something of a map with the general layout, which, in turn, serves as a matrix through which the input text is understood.

For example, the translation algorithm is trained on a corpus of text and its counterpart in another language. Then the whole thing is augmented on each side with the accompanying vocabulary layout, which includes synonyms, semantics, and other supplementary material.

Overall, Natural Language Processing consists of two basic divisions:

  • Natural Language Understanding
  • Natural Language Generation

Natural Language Understanding

Natural Language Understanding is the analytical branch of Natural Language Processing. It is all about analyzing the contents of the text and understanding its insights.

Comprehension is the key element of NLP. The thing is – language is an ambiguous and multi-pronged beast. The meaning of the message depends on the context it is expressed in and other factors that address the purpose of the message.

To take these factors into the equation make the algorithm capable of getting the true meaning of the message – different techniques are used to deconstruct and analyze the text.

That’s what Natural Language Understanding (AKA Natural Language Interpretation) deals with. It lays the foundation for further proceedings.

NLU is a subdivision of data mining (you can read about it right here) that deals with textual content. As such, it is used prominently in the majority of data science operations. It is everywhere – from the spam filters to the grammar checks.

Natural Language Understanding involves

  • processing the text (i.e., structuring a piece of unstructured data)
  • analyzing its content to extract insights of relevance (for example, names mentioned in the article or figures related to market growth)
  • subsequently preparing it for some utilization (for example, to generate custom responses).

NLU is applied in the text classification.

Natural Language Generation

Natural Language Generation is the operational branch of NLP. In strict terms, NLG can be described as:

  • creation of the custom messages
  • with the information that is relevant to the query (telling the time when asked “what time is it?”)
  • in a form appropriate to the context of the query (answer to the question, summarization of the text, and so on).

Natural Language Generation is built on the foundation of Natural Language Understanding. In broad terms, the effectiveness of the generative model depends on the quality and precision of the applied analysis. This means – it is not a good idea to use NLP Model trained on Shakespeare’s sonnets to generate medical bills.

The generative procedure involves the following steps:

  • An algorithm determines what information must be generated into text.
  • This includes determining the solid and fluid elements, i.e., parts of the text that must be included unchanged (relevant terms, names, figures, etc.) and pieces of the book that can be transformed depending on the context).
  • Then the message is organized into the appropriate structure. The structure can be casual (plain narrative sentence), or it can be formalized (for example, as a list)
  • In the case of voice synthesis – uses a prosody model, which determines breaks, duration, and pitch. Then, using a speech database (recordings from a voice actor), the engine puts together all the recorded phonemes to form one coherent string of speech.

Natural Language Processing Challenges

Two basic challenges occur during the development of NLP models. Both of them are directly related to the preeminent features of the natural language. These are:

  1. Natural Language is irregular and ambiguous. There are many different words with numerous alternative uses.
  2. Natural Language structures are mutable and therefore complicated. Various phrase types can be formed out of the same bag of words.

This creates a problem for NLP as it is unable to comprehend the real meaning of the text. Misinterpretations pile up, and this manifests itself in incorrect, unusable results.

The solution for these challenges lies in more in-depth and more thorough corpus analysis.

  • The parsing mechanism must be able to explore various syntactic arrangements for phrases and be able to backtrack and rearrange them whenever necessary
  • Grammars must contain large libraries of relevant expressions to improve the precision of the checking. This way, the anomalies are easier to detect.
  • Grammatical rules must be tuned to detect inconsistencies in the structure and word use.

Why NLP is important?

Large volumes of textual data

Natural language processing helps computers communicate with humans in their language and scales other language-related tasks. For example, NLP makes it possible for computers to read the text, hear the speech, interpret it, measure sentiment, and determine which parts are essential.

Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to analyze text and speech data efficiently thoroughly.

Structuring a highly unstructured data source

Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each style is a unique set of grammar and syntax rules, terms, and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter, and borrow terms from other languages.

While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps to resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

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In Conclusion

The main currency of the modern world is information. The most valuable elements of information are insights and understanding of the context they are in. Semantics is the key to understanding the meaning and extracting valuable insight out of available data. This is what the majority of human activity is about – in one way or another.

However, there is way too much data to comprehend and far too many tasks to accomplish to get the big picture manually. That’s why computers are integral parts of any business operation.

The critical element in interpreting data and the meaning behind it is the natural language processing algorithms.


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PODCAST #18. AI’s Influence in Virtual Healthcare and How Product Managers Can Help in the Revolution

In this Careminds podcast episode, our conversation with Ran Shaul, Chief Product Officer and Co-Founder of K Health and Hydrogen Health, explores virtual healthcare and the influence of AI on patient experiences.

The discussion extends to data-driven decision-making, entrepreneurship within the healthcare sector, and Ran’s unique perspective on the central role product managers play in health tech.

How to Know When a Career Path Makes Sense

After a late start in his career post a five-year service in the Israeli Army, Ran pursued industrial engineering and computer science in Israel, driven by a passion for data science. Upon graduation, he used his skills to tackle complex problems using data, with a particular fascination for employing mathematics in business contexts.

“That’s really the theme of everything I’m passionate about. I don’t know why I’m attracted to the concept of using mathematics to solve business problems.”

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

This led him to start his first business after only a few years of experience in a company working with data warehouses in the early days, which involved managing large databases and local machines before the advent of the cloud. This step into entrepreneurship was motivated not just by a desire for creative freedom, but also by a conviction that data science was poised to become highly influential. This conviction proved true as Ran navigated the growing fields of data mining and natural language processing.

Ran started three companies in total, with the first one being in the health sector. The other two were either acquired or sold, and his focus eventually settled on a company he had founded 6.5 years prior. This company represented a matured perspective in entrepreneurship and offered the chance to tackle a significant problem.

Driven by personal experiences with healthcare and a desire to contribute to something mission-driven, Ran aimed to use data to empower people to make better decisions, particularly in the field of medicine. Six years prior, accurate online medical information was scant and he saw potential in creating an online system for medical advice that was as easily accessible as booking a flight or finding a restaurant.

When asked about the nature of his company, K Health, Ran explains that it’s an AI company, a virtual company, and a doctor’s clinic all in one. Traditional doctor visits often have negative expectations, including long wait times, short consultations, and unforeseen costs. K Health aims to alleviate these issues by offering a more flexible and comprehensive experience.

Patients can consult a doctor on their own terms, at any hour of the day. This flexibility caters to those with busy schedules who might only find time for a doctor’s appointment late in the evening. The wait time is minimal, and the consultation is more in-depth as patients can discuss their symptoms at length with an AI before meeting a physician. This enables the physician to understand the patient’s condition quickly and thoroughly.

The company offers multiple modes of consultation, including video and text-based conversations. Unlike traditional doctor visits, their service doesn’t necessarily end after a single consultation. Patients have the freedom to return to the app and continue discussing their condition or ask further questions about their treatment. This fosters a long-term relationship with the physician rather than a series of transactional interactions.

What Does It Take to Align Innovation and Market Perception?

In healthcare, you should adopt an approach that is conservative, avoiding the typical tech mindset of “move fast and break things”. This principle is even more important when navigating the intricacies of healthcare regulations, which often contain gray areas. Despite these challenges, it’s vital to always prioritize safety and adhere strictly to regulations.

On the question of balancing innovation with regulation, especially as patients share their information with an AI, Ran believes that their approach in summarizing a patient’s situation to provide efficient and personalized care is an innovative and useful feature. He indicates that users are in full control of their experiences, which makes this combination of virtual primary care and personalized AI a truly innovative healthcare solution.

For instance, while there are companies who have chosen to adopt a more aggressive approach by prescribing potentially addictive medications online, this might not always be the best course of action. Such decisions should be made with the patient’s health and safety in mind. Restrictions to service areas that guarantee high-quality and safe care should be seriously considered.

Now, the medical decision-making process primarily lies in the hands of qualified physicians. As an entrepreneur or a tech professional, one should respect and adhere to these decisions without any judgement or influence. The guiding principle in digital health should always be thinking about the long-term outcome for the patient rather than a fast-paced growth model.

While this approach might not conform to conventional business growth models, in the field of healthcare, patient outcomes should always take precedence. It’s important to steer clear of cases that might jeopardize patient safety or the reputation of digital healthcare. By considering these aspects carefully, one can successfully navigate the complexities of designing user-centric, innovative, and safe healthcare solutions.

What Are the Key Challenges in Creating Unreplicated Workflows?

“It’s fine to be an AI company or a virtual clinic individually, but integrating both presents a significant challenge”. 

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

Envious glances might be cast towards AI companies that develop an algorithm and simply provide an API for use, or services that offer “doctor in a box” solutions via video call. However, without a connection between the two, real change can’t occur.

So how do you apply AI safely for the benefit of physicians and patients within a clinical care environment? It’s not just about building an AI system that’s accurate and continually learning, but also about making it understandable for patients and beneficial for physicians.

Often, questions arise about how such an accurate machine was built, one that knows everything about primary care conditions and can diagnose people. However, the main question isn’t just about how it was built, but also about how it’s explained to patients. How do patients understand what the results actually mean? How are these results handed over to physicians? And how is the experience continued such that when a patient has consulted with the AI, the physician has the ability to seamlessly take over and make the actual medical decision?

These considerations represent the major challenge. In the end, the service needs to be something people enjoy using and are satisfied with. It’s a blend of art and science, requiring a combination of different domains. A meeting at a company like this could involve five different domains in the same room: physicians, engineers, mathematicians, regulatory and operational experts, and product designers.

The second part of the challenge is how to build an accurate algorithm. This is where reinforcement learning comes in. Regardless of how simplistic the initial iteration might be, if the model is trained rapidly enough and consistently given feedback about its performance, it will learn and deliver the desired results over time. This concept of a machine constantly learning from humans, a continuous loop of diagnosis, feedback, and improvement, is at the core of the AI’s development and refinement.

These two aspects – multidisciplinary collaboration and constant machine learning – are instrumental in overcoming the challenges that come with blending AI and healthcare in an effective and meaningful way.

How to Define Product Success in Your Organization

“If you have people using the product and come back for more, that is when you know, you have a good product in the market.”

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

Reflecting on leadership style and how it has evolved over the years, there is a need to balance personal opinions and passion with the success of the company. In the early stages, when the company is small, you might be doing a little bit of everything. However, when the company grows – as it did during the COVID-19 pandemic from a 50-person company to a 300-person company – the need for vision and leadership becomes more pronounced.

Using techniques like providing hints rather than direct instructions and allowing people to discover things themselves can be very effective in larger settings. As the company grows, the leadership role becomes more about providing vision and inspiration rather than direct, hands-on guidance.

The establishment of a strong leadership layer is critical to the impact and success of the company. This strong leadership group, composed of leaders in different domains, has the ability to execute efficiently and effectively. Creating alignment with this group is key. It’s important to maintain the right to go into the details – to look at the code, the algorithms, the design – but to do it in a consultative way rather than authoritative, to avoid disrupting the work of others.

Maintaining a strong leadership team at the top, ensuring they have the capacity and willingness to execute, while occasionally diving into the lower levels to get your hands dirty, is vital. It’s a balance of leading by example and supporting those executing the work.

Tough Jobs, Tougher Candidates: The Ideal Profile for a Product Manager

“You need to have a belief, you need to have a vision. They need to be able to basically say no to the naysayers and say no.”

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

Ultimately, someone needs to connect the dots. There’s a necessity for someone to sit in a room, hear all the arguments from various sides, and then stitch it all together. This task is complicated because product managers may not have a background in medicine, nor might they fully understand all the regulatory aspects of their decisions. Despite this, they suddenly need to merge data science, the accuracy of algorithms, and the provision of high-quality clinical care. This makes the role of a product manager incredibly complex, given that they likely aren’t a data scientist nor a physician.

There are two dimensions that are important here: curiosity and the ability to make decisions. Surprisingly, many people prefer to stick to what they know. If they’ve worked in an e-commerce company, for instance, they might be comfortable with selling a new product using the same basic user funnel principles. However, the role here requires learning new domains, understanding the considerations of a physician, the considerations of an algorithm, and integrating those. This requires an eagerness to learn, to read and to understand beyond what one already knows.

The second dimension is decision-making and trade-offs. There’s rarely a perfect solution or an exact minimum viable product (MVP) in every aspect. So, you have to make decisions and execute them in such a way that you’re making small progress with each step. It’s not about one or two decisions; it’s about thousands of micro-decisions that build the big picture and result in a cohesive product. This combination of curiosity and trade-off handling makes for a very strong product manager or product owner.

How Often Do Product Managers Influence the Company’s Vision?

“A product manager needs to kind of ignore the noise and follow the data and, but that’s the task when you actually have a running product with your own data.”

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

It can be challenging to know which feature to implement, and sometimes you have to rely on A/B testing and observing what works. This requires a product manager to cut through the noise and follow the data. However, this mainly applies when you already have a running product with your own data.

The situation changes when you don’t have this data, for instance, when you want to start a completely new feature or even a new company. While surveys can provide some feedback, consumers may not be as good at giving feedback for a product that doesn’t exist yet. It’s difficult for consumers to envision using a product that doesn’t exist.

In these situations, the product manager needs to rely more on gut feeling, belief, and vision. They need to have the courage to say no to the naysayers and to believe that they are innovating something that people will want to use. This is where many interesting things happen and where new features are born.

For instance, with K, we didn’t initially know if people would be interested in a single screen showing them a differential diagnosis. Some suggested that people wouldn’t want this feature and that it would only confuse them. However, we went ahead, implemented that screen, and iterated around it. It turned out to be a moment of success, with users spending four minutes answering questions just to know what K thinks about their condition. This was despite initial feedback that people wouldn’t want to spend that much time providing information.

So, the toughest part of being a product manager is to break through the “nos”, follow your vision, and build something that you believe people will like. Then, you put it in their hands and see how they respond. Despite the rules and guidelines, sometimes you need to see past them, invent new things, and rethink the existing order.


In conclusion, if you have a good idea, just go ahead and do it. While gaining experience in big companies and working in different environments is valuable, there’s something uniquely rewarding about pursuing your own idea. Entrepreneurship and leadership aren’t for everyone, but if you enjoy the excitement and have something you want to pursue, go ahead and do it. Put it out there.

The key points are thus:

  • Passion, persistence and the right skills can create meaningful entrepreneurship ventures, even in complex fields like healthcare.
  • The integration of data science, AI and real-world medical expertise is key to providing a more accessible and efficient healthcare service.
  • Regulatory compliance, safety, and patient-first approach are paramount in navigating the challenges of digital healthcare innovation.
  • Success in health-tech depends on multidisciplinary collaboration and constant machine learning, aiming for a blend of accuracy, transparency, and patient-physician interaction.
  • The role of a product manager in this setting is multifaceted, requiring curiosity, sound decision-making, and the ability to navigate both familiar and unfamiliar terrains.








The APP Solutions launched a podcast, CareMinds, where you can hear from respected experts in healthcare and Health Tech.

Who is a successful product manager in the healthcare domain? Which skills and qualities are crucial? How important is this role in moving a successful business to new achievements? Responsibilities and KPIs?

Please find out about all this and more in our podcast. Stay tuned for updates and subscribe to channels.

Listen to our podcast to get some useful tips on your next startup.

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