If you ask a group of people what NLP is, chances are that you will get two different answers. They will be most likely based on the respondents’ vocation, interests, or experience.
But what’s even more interesting, both answers will be correct. How’s that possible?
One NLP variant might be more widespread among the general public. This is mainly owing to the popularity of modern psychology and the rise of coaches promising huge success to those who implement strategies advocated by this approach. The other NLP variant, however, is known to more tech-savvy people. In essence, it is what enables a plethora of AI tools to interact with people in human language.
But what do the two have in common? Except for sharing origins from linguistics, alleviating communication, and emerging in the approximately same period, nothing much. In simple terms, the former improves communicate between people, whereas the latter facilitates communication with machines and smart devices. Though you might not be aware, you’re implementing both NLPs on a daily basis.
NLP: Neuro-Linguistic Programming
Mainly owing to Tony Robbins, an American author and motivational speaker, people tend to identify NLP as neuro-linguistic programming. This NLP refers to an approach in psychology which, in essence, includes researching strategies that successful people implement and their application to attain personal objectives. The approach connects language, thoughts, and behavior patterns acquired through experience with particular outcomes.
NLP advocates presume that every human action is essentially positive. Hence, if something unexpected occurs or a plan fails, the overall experience is neither good nor bad. It just stands for extra useful information. The practitioners believe that there are natural orders of communication, learning, and change, six of which are:
- Environment, standing for the lowest level of change. The environment is an individual’s context or setting, involving other people around them.
- Identity, referring to what an individual perceives themselves to be. It also includes their responsibility and roles they have in life.
- Capacities and skills, representing one’s capabilities and what they’re able to do.
- Behavior, pertaining to particular actions one performs.
- Beliefs and values, regarding one’s own belief system and issues that concern them.
- Purpose and spirituality, representing the highest level of change. It refers to involvement in religion, ethics, or other systems.
Brief history and implementation of NLP
NLP dates to the 1970s. John Grinder, a linguist by profession, and Richard Bandler, a mathematician and information scientist, developed the concept of NLP at the University of California. The first book about NLP, titled Structure of Magic: A Book about Language of Therapy was published in 1975. In it, the founders tried to emphasize specific communication patterns which separated excellent communicators from others.
The duo’s work resulted in the development of the so-called NLP meta model, which was a technique they thought could determine language patterns that mirrored fundamental cognitive processes. The general public became interested in NLP following its advertisement as a tool that might help people discover how others gain success. Nowadays, NLP is implemented in a wide range of fields involving medicine, the military, law, counseling, business, education, performing arts, and sports.
If it weren’t for Tony Robbins, along with numerous motivational speakers and coaches, NLP might not have gained such popularity it has today. So, it doesn’t come as a surprise why, when the term NLP pops out, the first association is Neuro-Linguistic Programming. However, you should be aware that there is another NLP which, save stemming from linguistics, has nothing to do with Neuro-Linguistic Programming.
Ladies and gents, please welcome Natural Language Processing.
NLP: Natural Language Processing
If you’re more tech-oriented, chances are that you’re familiar with the other NLP that has nothing to do with modern psychology. In this case, NLP stands for Natural Language Processing, and it’s an AI component referring to the capacity of a computer program to interpret spoken and written human language.
This might be a bit confusing but bear with us. A natural language is something that we humans use every day. A natural language has never been created deliberately or for a specific purpose. Instead, it has naturally evolved through usage and repetition. To use a natural language, it doesn’t take any purposeful planning or strategy – everything comes, well, naturally.
Constructed or programming languages, on the other hand, are of different origins and have divergent development paths. They have been built with a specific purpose in mind – take C, C++, Python, or Java for example. All of them, and many more besides, were created for a particular purpose.
For a machine to work independently, a key principle is an ability to communicate with humans using a natural language they are familiar with. In the realm of Artificial Intelligence (AI), a field that enables machines to interact with humans using a natural language is NLP – Natural Language Processing. So, NLP is a blanket term that includes everything concerning enabling machines to process a natural language. It could be related to receiving and understanding an input or creating a response.
Natural Language Processing over time
Much like its psychology counterpart, NLP dates back to the mid-20th century, so it has existed for over 50 years. It has evolved from a range of disciplines such as computational linguistics development and computer science.
The 1950s seem to be a significant period for the fields of science. Among other not-so-scientific events, this period records the invention of UNIVAC I, the first business computer, the first U.S. transcontinental TV transmission, and the discovery of the DNA double helix.
In the 1950s, Alan Turing created the Turing Test to check if a computer is intelligent. The test included automated interpreting and generating human language as an intelligence criterion. Until the 1990s, natural language processing was mostly rules-based, implementing “handmade” rules that linguists created to discover how computers process natural language.
In the 1990s, owing to computing advancement, a more statistical approach replaced the initial, language-first approach to NLP. Computers became more efficient and could be implemented for creating rules based on linguistic statistics without requiring a linguist to develop them. In this decade, data-driven NLP became mainstream, and this was the period when NLP moved from a linguist to an engineer-based approach.
From the 2000s to this date, NLP has witnessed a tremendous rise in popularity. Along with advancements in computing power, NLP attained multiple applications in the real world. Nowadays, approaches to natural language processing encompass a combo of traditional linguistics and statistical methods.
Why is NLP important?
In essence, NLP allows computers to understand human language. To get a clearer picture, imagine a language (Spanish, English, Esperanto) as a free form of text – so, there are no keywords set at specific positions when giving input. This is how a machine perceives it.
What a machine doesn’t know but humans do is that there are multiple ways to communicate something using a natural language. For instance, you can ask about the weather outside in at least three ways:
- What’s the weather like today?
- Is it cold outside?
- Is it going to be sunny today?
- Shall I need my wind jacket?
The message of these four sentences is the same – inquiring about today’s weather. Our human intelligence allows us to recognize the underlying message and similarities easily and promptly respond. Machines, however, aren’t capable of that. To respond, a machine will require an algorithm, which, in turn, requires an input to be in one set format. You will notice that these four sentences differ both in their structure and format.
One possible solution is to code the rules for each word combination to assist a machine in grasping the point. However, this isn’t quite a plausible solution as things will become complicated quite quickly. And this is where NLP kicks in.
NLP, or natural language processing, is a subset of AI whose task is to enable a machine to communicate using natural language. In addition, it needs to ensure that a machine is able to process huge amounts of language data and acquire insight and information. Yet, before any natural language processing takes place, the text must be standardized.
How NLP works
NLP can be applied in multiple fields such as search engines, medical research, and business intelligence. But what does it do?
So, as already mentioned, NLP helps computers grasp natural language the same way humans do. No matter if the language is spoken or written, NLP implements AI to take the input, process it, and make it understandable for computers. During the processing, the input is turned into code at some point so that the machine can understand it.
NLP has two major phases: one is data preprocessing, and the other is algorithm development. The former encompasses preparing and cleaning the text data so that the machine can understand it. Data preprocessing arranges the text in a workable form, emphasizing features the algorithm can later use to work with. This could be done in several ways:
- Tokenization, implying breaking the text into smaller units a computer can work with.
- Lemmatization and stemming, involving reducing words to their root forms.
- Part-of-speech tagging, including marking words according to the part of speech they are (verbs, nouns, adjectives, adverbs, etc.).
- Stop word removal, encompassing removing common words from the text for more unique words offering information.
Once the preprocessing phase is finished, an algorithm is created to process the data. There are a variety of processing algorithms but the two most frequently used include rule-based systems and machine learning-based systems.
Rules-based system has been implemented since the early phase of NLP development, and it involves using meticulously designed linguistic rules. Machine learning-based system algorithms implement statistical methods. ML algorithms learn to carry out tasks according to the training data they are provided, then fine-tune their methods with more data being processed. By combining machine learning, deep learning, and neural networks, NLP algorithms sharpen their own rules via recurrent processing and learning.
What is NLP used for?
Natural language processing is implemented in many more instances than you think. The first one that you must have tried out, either for work, study, or fun, is a machine translation. This process involves a computer translating a text from one language, say English to another, Spanish, for example, without human interference. Tools used here include (but are not limited to) Google Translate, Translate Me, or Bing Translator.
Another instance is natural language generation, a process involving NLP algorithms that analyze unstructured data and automatically generate content based on it. Sounds familiar? Yes, we’re talking about OpenAI’s popular application called ChatGPT, which has caused a lot of hype recently. In addition, by being able to analyze copious amounts of research papers and academic material, AI can be implemented for academic research and analysis. In this case, the analysis is carried out based not only on the metadata but also on the text itself.
NLP can be used for text extraction as well. This process includes summarizing a text and discovering significant data pieces. One practical instance is keyword extraction – taking the most significant words or phrases from a text – that is used for search engine optimization (SEO) purposes. It’s worth mentioning that keyword extraction is not fully automated, as doing it with NLP demands some programming. Luckily, there are myriads of keyword extraction tools which automate nearly the entire process. A user only needs to set parameters in the program.
NLP is also implemented for text classification, i.e., assigning tags to texts to categorize them. This is quite handy for sentiment analysis, helping NLP algorithms to identify the sentiment behind a text. To illustrate, if brand A appears X times in a text, the algorithm can discover the amount of positive and negative mentions. A perfect example is customer feedback analysis in which AI analyses social media reviews.
Other instances of NLP implementation
Besides instances that we’ve just mentioned, NLP can be implemented for customer service automation. It involves voice assistants using speech recognition to grasp what the customer is talking about to address the call accordingly.
AI is also able to help in predicting forecasting and ideally preventing disease based on the analysis and categorization of medical records. Likewise, NLP can be used for stock prediction and financial trading insights. In this case, AI analyzes market history together with 10-K documents containing an extensive summary of the financial performance of a company.
Last but not least, NLP is used for talent recruitment in HR and for automating habitual litigation tasks – have you ever heard of AI attorneys?
OpenAI ChatGPT
Though the phrase OpenAI ChatGPT may look like a codename for a secret portal taking you to outer space, it is not so. ChatGPT is a small but powerful chatbot developed by OpenAI, an artificial intelligence research and deployment company. Chatbots are computer software implementing AI and NLP in order to understand customer questions and reply to them automatically, thus simulating human conversation.
Chatbots are typically used in customer support to provide answers to some of the most frequently asked questions. So instead of chatting with a customer support agent, you are ranting to a bot. Last Christmas many users gave their hearts to ChatGPT 3, but it didn’t give them away the very next day. Quite the contrary, this little but powerful AI tool has been blowing minds.
Unlike previous bots that were implemented as customer supports agents, or content generators (Jarvis/Jasper AI), ChatGPT can be used for a variety of purposes. To illustrate, if you’re looking for a specific piece of information, just type the question and the answer will pop out instantly. Similarly, if you’re a content/copywriter struggling with a writer’s block, use ChatGPT to generate ideas to implement in your content. And while you’re there, implement it to find keywords for your text. SEO experts swear to its power and efficiency.
The span of ChatGPT application doesn’t stop here. Have you ever heard of an AI tool that is able to code and produce pieces of software? Well, now you have. Yes, that’s right, ChatGPT can be used for software development. What’s even more stunning, a few days ago, it passed a medical exam! So, given ChatGPT’s almost unlimited capacities, Microsoft’s decision to invest a whopping $10 billion in its maker OpenAI is not surprising.
Will ChatGPT replace experts?
Hearing about what ChatGPT is capable of, one cannot help but fear the future for their job. But are SEO experts, writers, developers, even doctors really in danger? Well, not really, at least not soon.
Namely, ChatGPT might be able to code, but only simple applications. Producing complex software and video games like, say Elden Ring, is not quite possible with it. Therefore, developers are definitely here to stay, and so are doctors. Though it has passed the medical exam, ChatGPT isn’t likely to replace doctors. It may assist them in treatment, but it certainly won’t take over their job.
And writers, what about them? Well, it might depend on their employers. If they prefer artificial, unemotional, passionless, AI-generated texts that will only help them save money, such employers will certainly ditch writers in favor of ChatGPT at some point. But then the entire Internet will be overloaded with more or less identical texts with information whose accuracy is doubtful. After all, has Google Translate taken over translators’ jobs?
Benefits and challenges of NLP
The fundamental benefit of NLP is advancing the communication between humans and machines. You already know that the most immediate way to operate a computer is via its language, which is code. However, by allowing machines to grasp human language, interaction becomes way more intuitive and spontaneous for humans.
Other advantages of NLP include:
- Enhanced accuracy and efficiency of documentation;
- Capacity to summarize large and complex texts;
- Enabling personal assistants like Siri or Alexa to understand humans speaking;
- Alleviating the performance of sentiment analysis;
- Supplying analytics insights which were previously unavailable owing to data volume;
- Enabling businesses to implement chatbots for customer support.
As is the case with everything, NLP doesn’t come without challenges. Most of them revolve around the fact that human language is evolving and may sometimes be ambiguous. Computers require precision, structure, and clarity – just like the programming language. Natural language, on the other hand, might occasionally be imprecise and vague, with elements of slang, dialects, and social context, which makes it difficult for a computer to understand it.
The tone of voice and articulation can be tricky as NLP and AI are not able to pick up sarcasm or irony. When performing speech recognition, algorithms may skip subtle changes in the speaker’s tone. Accents can also be of great trouble, as an algorithm cannot always parse them efficiently.
Natural language continuously evolves with time and usage. Though every language has its specific rules, they can change over time. Hence, cardinal computational rules that are now implemented may be obsolete in the future with the changes in the real-world language.
