2023 EACL EACL 2023

Investigating the Effects of MWE Identification in Structural Topic Modelling

Abstract

AbstractMultiword expressions (MWEs) are common word combinations which exhibit idiosyncrasies in various linguistic levels. For various downstream natural language processing applications and tasks, the identification and discovery of MWEs has been proven to be potentially practical and useful, but still challenging to codify. In this paper we investigate various, relevant to MWE, resources and tools for Swedish, and, within a specific application scenario, namely ‘vaccine skepticism’, we apply structural topic modelling to investigate whether there are any interpretative advantages of identifying MWEs.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — structural topic modeling
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Natural Language Processing