Evolution of Natural Language Processing: From Bag of Words to Transformer

In the brief history of machine learning, applying methods and models to process Natural Language (NLP) has undergone an incessant journey. From simple models like Bag of Words (BOW) to complex architectures like Transformer, advancements in this field have profoundly influenced how we interact with computers and understand language.

Starting with Bag of Words (BOW), this model evaluates text based solely on the presence of specific words, without considering their order or context. Though simple and easy to implement, BOW fails to capture the deep meaning of text, leading to inaccurate results in complex contexts.

With the advancements in Deep Learning and Recurrent Neural Network (RNN) architectures, we’ve been able to handle more complex models. The emergence of Word Embeddings has addressed some issues regarding context and meaning of words in text. However, these models require significant computational resources and are prone to overfitting with limited data.

Seizing the opportunity from the success of Deep Learning, Transformer Architectures have marked a significant leap forward in the NLP field. With the ability to focus on long-range relationships in text without the need for hidden state layers, Transformers have brought high efficiency and applicability to various NLP tasks. However, the drawback of Transformers lies in their requirement for extensive computational resources, especially with large models, making deployment and training challenging.

In the future, research and development in the NLP field will continue to build on the achievements made. The combination of understanding natural language and advancements in machine learning methods will open up new opportunities, from creating intelligent virtual assistants to enhancing the efficiency of commercial applications. Throughout this process, balancing performance and computational resources will remain a challenge, and finding effective methods and models will continue to be a top priority for the NLP research community.

• Transformers in NLP
• Understanding Positional Encoding
• Attention Mechanism in Transformers
• Encoder in Transformer Model
• Decoder in Transformer Model
• Training and Inference with Transformers
• Advancements of Transformer Model
• Evolution of NLP