This paper presents the Visual Word Sense Disambiguation (Visual-WSD) task. The
objective of Visual-WSD is to identify among a set of ten images the one that corresponds to …

With state-of-the-art systems having finally attained estimated human performance, Word
Sense Disambiguation (WSD) has now joined the array of Natural Language Processing …

Vector-based word representation paradigms situate lexical meaning at different levels of
abstraction. Distributional and static embedding models generate a single vector per word …

The ability to use symbols is the pinnacle of human intelligence, but has yet to be fully
replicated in machines. Here we argue that the path towards symbolically fluent artificial …

Pretrained contextualized language models such as BERT and T5 have established a new
state-of-the-art for ad-hoc search. However, it is not yet well understood why these methods …

Most words have multiple meanings, but there are foundationally distinct accounts for this.
Categorical theories posit that humans maintain discrete entries for distinct word meanings …

Language evolves over time, and word meaning changes accordingly. This is especially
true in social media, since its dynamic nature leads to faster semantic shifts, making it …

The representation degeneration problem in Contextual Word Representations (CWRs)
hurts the expressiveness of the embedding space by forming an anisotropic cone where …

Large-scale pretrained language models have made significant advances in solving
downstream language understanding tasks. However, they generally suffer from reporting …

The availability of individual-level digital trace data offers exciting new ways to study media
uses and effects based on the actual content that people encountered. In this article, we …