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A Beginner's Guide to Unsupervised Learning Methods

Unsupervised learning techniques such as word embeddings and topic modeling have made significant advancements in recent years, enabling new levels of performance and opening up new possibilities for NLP research.

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The field of natural language processing has progressed rapidly in recent years thanks to breakthroughs in machine learning, massive datasets, and computing power.

While supervised learning that relies on huge volumes of human-annotated data powered much early progress, unsupervised learning has become increasingly important for enabling NLP systems to reach and eventually surpass human-level language understanding.

Unsupervised learning refers to machine learning techniques where algorithms learn patterns from unlabeled data.

Rather than mapping inputs to specific target outputs as in supervised learning, unsupervised methods discover abstract representations and structures within input data. These learned representations can then be used to analyze, explore or generate new data.

In NLP, unsupervised learning has unlocked new possibilities for building systems that can process language with human-like nuance and complexity. Approaches like word embeddings, topic modeling, neural machine translation, and language generation have led to systems that translate between thousands of languages, produce coherent long-form text, summarize documents, recommend content, and more.

These techniques allow NLP to evolve from rule-based and shallow pattern matching into learning directly from language corpora containing billions of parameters.

This article will explore two of the most impactful NLP techniques powered by unsupervised learning in recent years: word embeddings and topic modeling.

Word embeddings are dense vector representations of words capturing their semantic meaning. Topic modeling is a statistical method for discovering abstract topics within a collection of documents.

These methods, and progress in unsupervised learning generally, have enabled breakthroughs in machine translation, natural language generation, document classification, and beyond.

With massive datasets and open source tools, any organization can now leverage techniques like word embeddings and topic modeling to gain data-driven insights.

Advancements in unsupervised learning will continue expanding NLP's possibilities and competitive advantage for businesses. By understanding foundational techniques, their applications, and how to optimize for your unique data and use cases, you can harness the power of AI to transform how your organization understands and generates human language.

Advancements in Unsupervised Learning

Recent years have seen massive leaps forward in unsupervised learning for NLP. Two of the most notable advancements are the use of transfer learning and transformer models that can generate long-form text from scratch.

Transfer Learning

Transfer Learning enables models to be trained on one task and then re-purposed for another, related task.

Instead of building and training a new model from the ground up, transfer learning allows you to simply re-train or "fine-tune" an existing pre-trained model on your data. Since the model has already learned representation and features from its initial training, it requires less data and time to adapt to the new task.

For example, a model pre-trained on a large text corpus can be fine-tuned with labelled data for text classification, question answering, or summarization.

The pre-trained model has learned language representation which is then tailored for the specific downstream task. This approach reduces training costs and often leads to higher performance since the model can leverage what it has already learned about language.

Transfer learning has unlocked new possibilities for NLP, enabling more organizations to develop custom models and decreasing the time to build production systems.

GPT-4 and Language Generation

OpenAI’s GPT-4 (Generative Pre-trained Transformer 4) is an example of a transformer model that can generate coherent long-form text after being trained on a huge dataset. GPT-4 has over 200 billion parameters and can generate high-quality articles, short stories, social media posts, and more based on a prompt using unsupervised learning.

The model understands word context and relationships, enabling more fluid, natural language generation than previous models.

Figure 1: A basic transformer model architecture. Image from Towards Data Science.

GPT-4 builds on the original GPT (Generative Pre-trained Transformer) model but with a larger dataset and more advanced architecture.

Transformers have been hugely impactful for NLP and are employed in state-of-the-art models for translation, question answering, text classification, and other applications. However, they are expensive, difficult to optimize, and can reflect and even amplify the prejudices present in their training data.

Research in unsupervised learning will continue progressing NLP abilities and available tools. However, human feedback still plays an important role in steering models and expanding their knowledge in a trustworthy, unbiased manner.

While models like GPT-4 demonstrate the potential of self-supervised learning, collaborative progress between humans and AI may be key to building systems that generate language for and understand people rather than simply trying to replicate human language. The future of NLP will likely depend on how and if we choose to make technology work with us rather than replicate and replace us.

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Techniques for NLP

One of the biggest breakthroughs in NLP this year has been the creation of machine learning models that create articles from scratch, with GPT-4 (Generative Pre-trained Transformer 4) currently leading the way.

What’s unique about transformers is that they’re able to understand the context of words in a way that wasn’t possible before.

For example, approaches to transfer learning in NLP include:

  • Using pre-trained word embeddings: Word embeddings capture semantic relationships between words. Pre-trained embeddings can be used to initialize the embedding layer in a model for a downstream task.
  • Fine-tuning pre-trained models: Models pretrained on large datasets for a task like language modeling contain useful features for other NLP tasks. Fine-tuning involves unfreezing certain layers in the pre-trained model and retraining on data for the target task.
  • Adapter layers: Small "adapter" layers are added to a pre-trained model and trained on the target task's data while keeping the original model frozen. This requires limited compute but may produce sub-optimal results versus fine-tuning.
  • Adapter fusion: Multiple adapters are added to different layers in a pre-trained model and trained separately before "fusing" their parameters. This achieves performance comparable to fine-tuning while requiring less retraining of the original model.

When training a model for NLP, combining both supervised and unsupervised methods seems to provide more accurate results.

Supervised learning, commonly used for tasks such as topic classification, requires a large amount of tagged data and many iterations until a model can make accurate predictions. In unsupervised learning, on the other hand, algorithms learn patterns from unlabeled data.

Until recently, these unsupervised techniques for NLP (such as GLoVe and word2vec) used simple models (word vectors) and training signals (the local co-occurrence of words). Skip-Thought Vectors is a notable early demonstration of the potential improvements more complex approaches can realize.


Unsupervised learning techniques like word embeddings and topic modeling have made significant advancements in recent years, enabling new levels of performance and opening up new possibilities for NLP research. These advancements have the potential to greatly impact the field of NLP by enabling faster and more accurate processing of natural language data. As research in this area continues, we can expect to see even more exciting developments in unsupervised learning techniques for NLP.

At Lettria, we specialize in leveraging unsupervised learning techniques to help businesses gain data-driven insights. Our NLP platform offers state-of-the-art word embedding and topic modeling capabilities that can help you unlock new possibilities for your business. Whether you're looking to improve document classification, recommendation systems, or text summarization, our platform can help.

To learn more about how Lettria can help you harness the power of NLP to drive your business forward, we invite you to sign up for a free trial or book a demo with our team. Our experts are always available to provide you with insights into unsupervised learning methods and help you optimize your approach to suit your unique needs. Contact us to get started on scaling your NLP technology and launching projects today.


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