Natural Language Processing

Natural Language Processing

Contextual Versus Static Word Embeddings: Understanding The Tradeoffs

The article ‘Contextual Versus Static Word Embeddings: Understanding the Tradeoffs’ delves into the nuanced differences between these two types of word representations. It explores the foundational concepts, explains the workings of contextual embeddings, examines the limitations and applications of static embeddings, and discusses the tradeoffs involved in choosing one over the other. Finally, it looks…

Handling Out-Of-Vocabulary Words With Bert And Other Transformer Models

In the rapidly evolving field of natural language processing (NLP), handling out-of-vocabulary (OOV) words remains a significant challenge. Transformer models like BERT have introduced innovative ways to tackle this issue. This article delves into the mechanisms and advantages of BERT’s approach to OOV words, comparing it with traditional models like Word2Vec, and exploring the practical…