Lexical semantic change detection is a relatively young field in computational linguistics
concerned with the automatic detection of meaning changes in words over time. I will first
introduce low-dimensional semantic vector space models (VSMs) which dominate the task within the
current evaluation framework. Then, I will distinguish between type- and token-based VSMs and
discuss possible reasons for the apparent dominance of the simpler type-based models. I will show
that token-based models can be tuned to reach similar performance as type-based models, while
losing much of their expressiveness. Both model types then yield reasonable performance on current
data sets. However, I will argue that this performance cannot be fully trusted, as these data sets
are strongly biased towards polysemy. I will end by giving an outlook towards a formulation of
semantic change detection as supervised learning task, which I consider to be the most promising
direction of future research.
About the Speaker
I’m a last-year PhD student at the IMS (University of Stuttgart) working together with Sabine
Schulte im Walde on automatic detection of lexical semantic change. I hold a Bachelor degree in
Linguistics and English and a Master degree in Computational Linguistics. My interest is focused on
the application of machine learning methods to solve problems involving the semantics of words.
About the Series
The Lecture Series is organized every winter term and consist of talks with international
speakers. The speakers are experts from various fields and professions. The talks are aimed at
covering the whole spectrum of visual computing and at discussing the relevance of quantification.
This years lecture series will be held entirely online.
This is an online event only.
Registration: If you would like to attend, please email
Leonel Merino to