Visualizing Linguistic Shift
This addresses the need for tools to analyze semantic changes in language over time and geography, but it appears incremental as it builds on existing neural language models for word embeddings.
The paper tackled the problem of identifying and visualizing how words change meaning across different text corpora, presenting a computational technique to detect significant linguistic shifts and using enhanced scatterplots and storyline visualization for visualization.
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.