# Dynamic Word Embeddings for Evolving Semantic Discovery

## TL;DR

This paper introduces a dynamic statistical model for learning time-aware word embeddings that effectively capture semantic evolution over time, outperforming existing methods in accuracy and alignment.

## Contribution

It presents a novel model that jointly learns temporal word embeddings and addresses the alignment problem, with comprehensive evaluation strategies and superior performance.

## Key findings

- Successfully captures semantic evolution over time
- Outperforms state-of-the-art temporal embedding methods
- Provides reliable qualitative and quantitative evaluation results

## Abstract

Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting "alignment problem". This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.