2021 IJCNLP IJCNLP 2021

Modeling the Evolution of Word Senses with Force-Directed Layouts of Co-occurrence Networks

Abstract

AbstractLanguages evolve over time and the meaning of words can shift. Furthermore, individual words can have multiple senses. However, existing language models often only reflect one word sense per word and do not reflect semantic changes over time. While there are language models that can either model semantic change of words or multiple word senses, none of them cover both aspects simultaneously. We propose a novel force-directed graph layout algorithm to draw a network of frequently co-occurring words. In this way, we are able to use the drawn graph to visualize the evolution of word senses. In addition, we hope that jointly modeling semantic change and multiple senses of words results in improvements for the individual tasks.

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🧭 Keyword Pioneer — word sense evolution
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing