Empirical Analysis of CBOW and Skip Gram NLP Models
Tejas Menon · 2020
CBOW and Skip Gram are two NLP techniques to produce word embedding models that are accurate and performant. They were invented in the seminal paper by T. Mikolov et al. and have since observed optimizations such as negative sampling and subsampling. This paper implements a fully-optimized version of these models using Py-Torch and runs them through a toy sentiment/subject analysis. It is weakly observed that different corpus types affect the skew of word embeddings such that fictional corpus are better suited for sentiment analysis and non-fictional for subject analysis.