Visualizing Linguistic Shift

TL;DR

This paper introduces a method to detect and visualize how word meanings change over time and across disciplines using neural language models and advanced visualization techniques.

Contribution

It presents a novel computational approach to identify significant linguistic shifts and visualizes these changes with enhanced scatterplots and storyline visualizations.

Findings

Effective detection of words with significant semantic shifts

Visualization techniques reveal nuanced changes in word meanings

Application across diverse disciplines demonstrates versatility

Abstract

Neural network based models are a very powerful tool for creating word embeddings, the objective of these models is to group similar words together. These embeddings have been used as features to improve results in various applications such as document classification, named entity recognition, etc. Neural language models are able to learn word representations which have been used to capture semantic shifts across time and geography. The objective of this paper is to first identify and then visualize how words change meaning in different text corpus. We will train a neural language model on texts from a diverse set of disciplines philosophy, religion, fiction etc. Each text will alter the embeddings of the words to represent the meaning of the word inside that text. We will present a computational technique to detect words that exhibit significant linguistic shift in meaning and usage. We then use enhanced scatterplots and storyline visualization to visualize the linguistic shift.