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Data science – Evolution of NLP from traditional methods to deep learning techniques

Isn’t it remarkable how a toddler who can hardly understand and speak even a word eventually goes on to speak in complete sentences? Their journey involves observing people around them, repeating their words, memorizing phrases, and learning to form sentences. Humans tried to replicate something similar with computers, and after decades of research, it finally happened with natural language processing (NLP). We could train machines to understand our questions and produce relevant answers. It led to the creation of conversational bots like Siri, ChatGPT, and Gemini. Although we moved from NLP to deep learning models in just a few years, the natural progression was far longer and more complex.

In this article, we will discuss how NLP evolved into a deep learning mechanism from the simple beginnings of rules-based systems to accelerate the growth of data science.

What is NLP?

NLP is a branch of artificial intelligence (AI) specializing in interactions between humans and machines through natural language. It is the science of helping computers understand, interpret, and generate contextual responses in human language.

It empowers machines to process our native language fed through text or voice data, learn its meaning, intent, and sentiment, and produce appropriate responses. NLP relies on software programs to translate text input from human language to another, reply to voice commands, and produce concise versions of large text sets in real-time. 

Applications of NLP in real life

In the current digital-first world, almost everyone interacts with NLP systems daily. It could be as simple as a grammar checker and autocomplete or as complicated as Siri and ChatGPT 4. Some of its applications are listed below:

  • Content Translation: NLP translates a piece of text from one language to another target language. Google Translate is a classic example of this use case. 
  • Sentiment Analysis: This application identifies the user’s intent or mood when inputting text. Many companies use NLP to evaluate customer reviews and potential buyers’ moods.
  • Named Entity Recognition: A sales and marketing use case of NLP, it can be beneficial in retrieving information about entities like organization names, locations, decision-makers, and revenue.
  • Spam Detection: NLP can identify spam emails when you give the email text as the input to the system. It will evaluate details like subjects and the sender’s name to verify if it is an unsolicited communication.
  • Text Generation: NLP is the technology behind your mobile device that suggests the following words when sending a text. Chatbots are also a potential use case for simulating dialogue with customers.

These are just a few applications of NLP, and with newer advancements in technology, it will be used for a wide array of activities. It has witnessed significant evolution over the years, which we discussed below.

Evolution of NLP through the years

The origins of NLP date back to the 1950s during World War II when computer scientist Alan Turing wrote in his paper ‘Computing Machinery and Intelligence.’ He explained that we can assume machines can think if they engage in a conversation through a teleprinter or other device while imitating a human accurately. 

Rules-based Models

In the early years of NLP, researchers relied on rules-based systems for language processing. The work centered on building grammatical rules from the 1950s to the 1960s. NLP analyzed and processed text based on handcrafted rules and syntaxes. 1966 marked a significant milestone with Joseph Weizenbaum’s creation of ELIZA, a dialogue simulation system. It engaged with users through texts and relied on pre-set interaction patterns. It was further enhanced when Terry Winograd developed the SHRDLU model. It was a natural language understanding system that allowed users to move blocks in a virtual world.

Researchers refined rules-based systems over the next few decades to enhance the machine’s language understanding. However, their inability to understand human language intricacies, like grasping context and varied linguistic forms, slows its progression. It eventually led to researchers shifting away from rules-based models. It paved the way for statistical language processing methods.

Statistical Approach

From the 1980s to the 1990s, NLP research took major strides in applying statistical methods to expand the language processing capabilities of computers. With access to large volumes of data, researchers could uncover linguistic patterns and how each word is interconnected with the other. It also saw different machine-learning methodologies emerge to help computers improve language processing.

  • Hidden Markov Models (HMMs): Developed as speech recognition, model HMMs empowered machines to translate spoken language into written text. The breakthrough development of speech-to-text systems spawned revolutionary technologies like voice assistants and transcription generation.
  • N-gram Models: These models allowed computers to understand language in the right context. They predicted which word would appear next, considering the preceding words. 

With technology advancing at an unprecedented rate, NLP saw significant improvement in applying deep learning.

Deep Learning

NLP entered a new era with deep learning and neural network techniques in the last ten years. These new models allowed computers to generate responses in natural language mimicking humans, produce new content, and make predictions. With the integration of deep learning in NLP, machines could process large datasets to draw out relevant information.

With neural networks, machines could recreate and understand the human brain’s complicated functioning, making them intelligent and enabling advanced forecasts.

Transformers and LLMs

In the last few years, transformers have marked the beginning of a new chapter led by OpenAI’s GPT (Generative Pre-Trained Transformer) series that doesn’t just produce text but also generates creative and coherent prose and programming code. The AI agency refined these large language models, launching GPT-3 with 175 Bn parameters, followed by multimodal LLM GPT-4 with expanded parameters. These LLM models exhibit the capability to create human-like content, create art, and enable problem-solving.

NLP for Tomorrow: A Paradigm Shift

Deep learning concepts like neural networks and LLMs have changed the face of NLP, unlocking a wide array of possibilities that anyone would have thought possible. Yet, today, these language processing models enhance their capabilities to pave the way to revolutionary applications like conversational AI. These use cases can be applied to transform healthcare, life sciences, and finance industries.

However, the road to futuristic NLP is not without its challenges. There are ethical concerns, data privacy issues, and the debate on the text’s authenticity generated by NLP. As NLP research makes giant leaps, we must also consider the necessity of solving these challenges.

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