A developer demonstrated how to visualize word embeddings by reducing high-dimensional vectors to two dimensions using PCA and t-SNE. The walkthrough explains that PCA captures broad linear trends whi…
A new benchmark for classifying research papers as good (highly cited) or non-good (retracted) using only textual features from titles and abstracts achieves up to 91.12% accuracy with FastText and Su…
Researchers have developed a multi-feature fusion framework that combines static lexical representations (Word2Vec) with dynamic contextual representations (GPT) via a non-linear cross-attention mecha…
Researchers tested Word2Vec on Toki Pona, a constructed language with only ~130 words, using 1.4 million sentences. They found that non-core tokens like loanwords improved embedding quality by drawing…