Analyzing Rank-Frequency and Type-Token Relationships in Literary Texts
This article explores the rank-frequency and type-token relationships within an author-based corpus comprised of popular novels by renowned writers. We investigate whether these corpora exhibit Zipfian behavior in frequency distribution, analyze rank-frequency relationships through line fitting, and assess lexical richness using Heaps' law.
Attention Mechanism in Transformers
This overview delves into the transformative role of attention mechanisms in the architecture of advanced language models, specifically the transformer model. Beginning with a discussion on word embeddings and the basic self-attention mechanism, the text explores how attention is harnessed to extract long-range relationships within word sequences. From the fundamental principles of dot product attention and scaled dot product attention to the multi-head attention, this deep dive reveals how attention enables natural language understanding.