Over the past two decades, we have witnessed an exponential growth in AI technologies and the capabilities of AI-powered solutions. Natural Language Processing (NLP), which has been emphasized since the days when artificial intelligence was only an idea, is one of the fields where we can trace the developments in artificial intelligence and whose direct results we feel in our daily lives.
Advances in NLP technologies are already being used and shaping digital experiences. But there are questions that need to be answered: What capabilities does NLP have when considered in the context of digital experiences, how should we use NLP capabilities, and what should we pay attention to when getting support from NLP technologies?
Using NLP technologies, machines can understand natural languages to gain new information or suggest actions. Because machines can analyze vast amounts of data much faster than humans and turn it into results without delay or loss, they can be used as a tool to create better digital experiences.
In this article, we’ll explore why businesses need to incorporate natural language processing technologies and artificial intelligence into their efforts, and how recent advances can play an important role in improving the digital experience.
We’ll take you on a journey through NLP and digital experiences by following the topics below:
- What is Natural Language Processing (NLP)?
- Why is Natural Language Processing (NLP) Important?
- A Brief History Of Natural Language Processing (NLP)
- Today: What is GPT-3?
- Future Is Now: NLP In Our Daily Lives
- How Can NLP Support Digital Experience?
- Result: AI-Powered Digital Experiences
What is Natural Language Processing (NLP)?
The concept of natural language expresses the way we communicate in our everyday lives, how we speak, how we type, and how we share knowledge. Natural Language Processing (NLP), as a branch of artificial intelligence, is about understanding it at the most basic level.
Natural Language Processing involves bringing machines closer to human-like intelligence by giving computers the ability to understand text and speech. Today, with the huge amounts of data available online, it’s important to know what NLP can do for you and where its limitations lie.
Natural Language Processing can be defined as a growing opportunity and a multidisciplinary subfield. It focuses on computer processing and analyzing natural language inputs. Some of the contributions made by computer science, linguistics, artificial intelligence and communication sciences.
The goal of natural language processing developments is basically to make computers understand the contents created by humans. It is very valuable since most of the knowledge in the world is in natural language. The tricky part is understanding contextual nuances, cultural differences, and even sarcasm, as well as creating outputs that humans can understand.
As you can see above, AI and NLP are connected to each other at different levels. Similarly, there are different use cases of natural language understanding, processing, and generation.
Natural Language Understanding (NLU) 👉 Extractive Summarisation
Natural Language Processing (NLP) 👉 Entity Recognition
Natural Language Generation (NLG) 👉 Abstractive Text Generation
Actually, natural processing technologies and teams working on these have existed for a long time. However, NLP has recently experienced a surge of interest due to a few reasons:
🌐 The rise of the number of internet-connected devices.
💻 Increasing accessibility of these devices.
🤖 Acceleration of developments in the AI field.
💬 Increasing amounts of contextual, cultural, and sarcastic input via social media.
Why Is Natural Language Processing (NLP) Important?
As the amount of social media content, text messages, e-mails, and personal blogs increase, the amount of information we can access is increasing and growing day by day. As big data grows, it becomes more difficult to draw conclusions that contain useful and meaningful information.
To tackle this problem, NLP technologies are being developed that can help understand the meaning behind natural human language, and that can process, understand, and even generate natural language using data from around the world. NLP can be used in almost any field, from medicine to education and from emergency response to marketing.
NLP is a critical technology for our societal progress, as most of the world’s information is in the form of natural human language. Emails, WhatsApp messages, Twitter updates, news articles, books, spoken conversations, etc. Consider all available information. NLP enables machines to understand and make sense of all this data.
Historically, machines have found it extremely difficult to make sense of language. Human language is a chaotic, diverse and unstructured language. But computers are learning to extract and analyze information from text with impressive results thanks to the rise of machine learning and big data and artificial intelligence technologies.
A Brief History Of Natural Language Processing (NLP)
Developments in the NLP field have been accelerating over the years. This section will cover the major milestones in natural language processing research and where the field stands today.
Natural Language Processing (NLP) in the field of science works to make computers understand human language. It has a long history, and the evolution of NLP can be traced back more than fifty years. In 1950, Alan Turing published an article named Computing Machinery and Intelligence. The history of Natural Language Processing generally started here. In the article, Turing simply asked “can machines think?” and developed the Turing Test, originally The Imitation Game.
In 1954, IBM and Georgetown University worked together to build an experiment for machine translation. They translated more than 60 Russian sentences into English by using IBM 701. There were only 250 words and 6 grammar rules in the machine’s memory, yet it was a great success in 1954 and it makes investments in machine translation.
Until the 1960s there was no notably successful NLP system. Then Eliza (1964) and SHRDLU (1968) developed. Eliza was a chatbot developed by Joseph Weizenbaum in 1964. Her most popular script was DOCTOR, which was a Rogerian psychotherapist’s parody. Rogerian therapy is a therapy system developed by Carl Rogers. According to Carl Rogers, the patient always knows what is best for himself/herself. So, a Rogerian therapist let the patience dominate the conversation and just leads them.
ELIZA was using this logic to create replies by using human’s own input. You can talk with a simulation of ELIZA here. I did:
ELIZA attempted to recreate the computer interaction with a Rogerian psychotherapist. The program attempted to identify keywords and then offered obscure responses based on simplistic patterns of association with those keywords. The goal was to mimic human communication as a way to make people believe that they were interacting with a real person, at the simplest level.
Special Format Systems such as BASEBALL, SAD SAM, STUDENT, ELIZA, and CARPS were actually not very developed but perceived as advanced due to their niche functions. These are systems that have been built for special purposes. Regardless of the complexiness languages, these are only dealt with in their own field’s words. These systems were developed with narrow-minded principles in mind and are considered to be the most extensive. These systems make use of the psychological phenomenon called the ELIZA Effect, which makes people think they are talking to a human being, making them believe what they see on their side is a real person that is answering their questions.
Then, in 1968, SHRDLU was developed at MIT (Massachusetts Institute of Technology) as one of the most significant systems for NLP research due to its strong connectionist approach to NLP. SHRDLU was a simulation of a robotic hand. It can understand natural language instructions to move blocks in a virtual world. In the history of Natural language processing, the early 1970s were years of excitement: People were enthralled by the possibilities of computers figuring out what humans wanted to say. Following the SHRDLU, a number of systems were created.
Many programmers began to create “conceptual ontologies” in the 1970s, which organized real-world facts into computer-understandable data. These ontologies were created by analyzing vast volumes of natural language text in order to arrange this knowledge for use in artificial intelligence applications. Some of them are MARGIE (1975), SAM (1978), PAM (1978), TaleSpin (1976), QUALM (1977), Politics (1979), and Plot Units (1981).
PARRY was one of the first realistic chatbots. While ELISA Rogerian was a therapist, Parry was in the patient role with reference to her. Colby developed PARRY, which is programmed to talk to a therapist to help therapists learn about patients. It was a simulation of a schizophrenic patient.
Today: What is GPT-3?
Generative Pre-trained Transformer 3 (GPT-3) is a machine learning model. It’s one of the biggest and most advanced open-source algorithms for machine learning software until now. It used over 175 billion parameters trained on 1000s of public datasets over the world wide web.
It was created by OpenAI, an artificial intelligence research lab in San Francisco. GPT-3 is a machine learning model that can produce any type of text from internet data. It uses only a tiny quantity of text as input to generate vast volumes of relevant and sophisticated machine-generated material.
GPT-3 trained by using public internet data. In the table below, you can see data sources.
Future Is Now: NLP In Our Daily Lives
Technology has allowed us to realize the potential of language in most areas of life. NLP enables you to extract insights from unstructured text in an easily understandable format. It is often used to make sense of millions of data related to user information, customer preferences, and other relevant data. Also, NLP can be used in combination with Machine Learning and Deep Learning to offer a more personalized user experience. NLP technologies are generally used for information retrieval, question answering, and machine translation.
Most of us are already using NLP-based tools and technologies every day, both on computers and smartphones. We even can say we’ve been surrounded by NLP-based software performing an array of functions.
As our technology advances, NLP will make even deeper inroads into our lives. For example, new NLP-based healthcare applications are emerging that can help physicians handle patient questions, or work with medical records to provide better care for patients.
The process of writing is also a practice that has a tremendous impact on how one thinks and operates. In other words, it is an essential component of daily life. A writing assistant tool will not only help us improve our typography skills and faster copy generation, but also help us read and understand faster and with better comprehension.
Natural language processing (NLP) includes using statistical algorithms for improving a text’s accuracy, clarity, coherence, and persuasiveness. Some of the accessible authoring tools based on NLP technologies can be counted as Copyai, Grammarly, Quilbot.
NLP also powers chatbots and virtual personal assistants like Siri, Alexa, and Cortana… These chatbots can accomplish tasks like answering questions, making recommendations, and retrieving movies by analyzing what you tell the devices in a natural language.
It also enables applications that you may use every day from search engines such as Google, and Bing to popular social media sites like Facebook and Twitter. This is what powers Google’s search algorithm and its deep understanding of language. Using this deep understanding, Google can now recognize answers to a question directly from your content. It uses NLP to process the meaning of words in content, annotate it and use it to provide search engine users with highly relevant content.
How Can NLP Support Digital Experiences?
The term NLP is often used to refer to a broad set of approaches with the same goal: to make machines able to mimic intelligent behaviors. NLP (Natural Language Processing) is one of the most known and applied disciplines within AI. NLP techniques are responsible for the way AI-powered assistants like Siri or Alexa can understand and react to human language, but also for the way machines can read and interpret the text to extract information.
Natural Language Processing, or NLP, has developed considerably over the last few decades. Its ability to parse text, recognize patterns, and derive meaning from our conversational data is proving transformative for a wide range of industries. Today’s business applications for NLP are vast; from automating customer support to improving competitive analysis to better understanding individual customers’ needs.
Throughout the customer journey, NLP will enable you to provide your clients an optimal and highly personalized conversational experience. Innovative NLP-powered solutions may reinforce consumer knowledge before, during, or even after a conversion is completed, capturing your customers’ natural language to increase your knowledge through their exact feedback.
It makes sense that one of the priorities for enterprises today is to improve automation and data quality. In addition to that, improving digital experiences and increasing customer loyalty is another major focus for many businesses today. Customer-centric organizations are constantly searching for new ways to engage customers in more meaningful, personalized ways. We all believe that the way they interact with the customer has a significant impact on engagement, and so we are investing in NLP technology to learn how to better understand customers.
AI software has made great strides in the past decade, and NLP has been at the heart of a lot of those advances. By combining natural language processing with machine learning, it is possible for a platform to parse language into segments and compare a customer’s information with other customers’ data, thereby increasing efficiency and accuracy.
They are discovering the potential for NLP technology to help them better understand their customer and increase conversion rates, loyalty, and satisfaction by providing a more customized learning experience.
To sum up, we can clearly say that: NLP technology can be used for creating better digital experiences.
Here is a list of areas you can use NLP for better digital experiences:
🧾 Lead Forms.
🎫 Support Tickets.
📊 Analyzing Customer Feedback.
😊 Analyzing Customer Happiness and Sentiment.
🔁 Text-to-speech Apps.
🔎 Website Search Bars.
🤖 Chat bots.
Result: AI-Powered Digital Experiences
Throughout the customer journey, NLP will enable you to provide your clients an optimal and highly personalized conversational experience. Innovative NLP-powered solutions may reinforce consumer knowledge before, during, or even after a conversion is completed, capturing your customers’ natural language to increase your knowledge through their exact feedback.
It makes sense that one of the priorities for enterprises today is to improve automation and data quality. In addition to that, improving digital experiences and increasing customer loyalty is another major focus for many businesses today. Customer-centric organizations are constantly searching for new ways to engage customers in more meaningful, personalized ways. We all believe that the way they interact with the customer has a significant impact on engagement, and so we are investing in NLP technology to learn how to better understand customers.
AI software has made great strides in the past decade, and NLP has been at the heart of a lot of those advances. By combining natural language processing with machine learning, it is possible for a platform to parse language into segments and compare a customer’s information with other customers’ data, thereby increasing efficiency and accuracy.
They are discovering the potential for NLP technology to help them better understand their customer and increase conversion rates, loyalty, and satisfaction by providing a more customized learning experience.
To sum up, we can clearly say that: NLP technology can be used for creating better digital experiences.
Here is a list of areas you can use NLP for better digital experiences:
🎫 Support Tickets.
📊 Analyzing Customer Feedback.
😊 Analyzing Customer Happiness and Sentiment.
🔁 Text-to-speech Apps.
🔎 Website Search Bars.
🤖 Chat bots.
Result: AI-Powered Digital Experiences
As we mentioned before in our blog, building personalized experiences is critical to reaching and engaging with today’s customers, since contemporary customers need immediacy, autonomy, and accessibility. There are too many ways to improve your digital experiences, and most of them could be applicable by benefiting NLP technologies, especially after GPT-3.
Miranda Miller recently said that “artificial intelligence is being used by media, universities, and other organizations for research automation and cross-referencing, crawling and classifying content in many languages to identify emerging trends, generating article and paper summaries, fact-checking, crunching data, and even writing and full articles.” While many reputable institutions are already making use of AI technologies, it is necessary to try how we can benefit from AI developments by adding a human control stage to the end of the process.
NLP uses automated machine learning algorithms to process data and extract information. This can help businesses cut costs. Businesses will increasingly need the help of AI software with NLP capabilities, including speech recognition, automatic classification, and self-learning.
Natural language processing (NLP) technology has enabled great improvements in data gathering, processing, searching, and presenting for all kinds of businesses. It is the ability of a machine to understand natural language and extract meaningful information from it. NLP seeks to create machines that can understand human language as they do another machine language such as binary code, which is more understandable to them.
With the advantages of machine learning available to enterprises, the speed and accuracy of data searches are formidable. Moreover, using AI for search and classification can enable businesses to improve system functionality through rapid response and high-performance processes.
It is also important to note that Google is very clear about considering AI-generated content as spam according to the Webmaster Guide. However, what Mueller ( Webmaster Trends Analyst at Google) said can also mean IF Google detects which content is AI-based, it may be marked as spam.
Of course, you shouldn’t copy and paste anything to your website or chatbot but you can use AI generators as semantic word determiners for both search engines and humans. The best practice could be a mix of AI and human resources together. Such as let AI write the first draft and correct it manually. Samely, let chatbots answer people for one or two lines of dialog but then contact them manually.
Unlike actual humans, NLP-powered tech solutions can reply to a consumer in real-time, 24 hours a day, seven days a week. NLP has the potential to improve digital experiences and transform manual operations into tech-enabled activities.
