Distributional semantics provides multidimensional, graded, empirically induced word
representations that successfully capture many aspects of meaning in natural languages, as …

Distributional semantics is a usage-based model of meaning, based on the assumption that
the statistical distribution of linguistic items in context plays a key role in characterizing their …

Despite its importance, the time variable has been largely neglected in the NLP and
language model literature. In this paper, we present TimeLMs, a set of language models …

The success of the large neural language models on many NLP tasks is exciting. However,
we find that these successes sometimes lead to hype in which these models are being …

Many facts come with an expiration date, from the name of the President to the basketball
team Lebron James plays for. However, most language models (LMs) are trained on …

Large language models (LLMs) are capable of successfully performing many language
processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify …

Using word embeddings from 850 billion words in English-language Google Books, we
provide an extensive analysis of historical change and stability in social group …

Word embeddings are becoming popular for political science research, yet we know little
about their properties and performance. To help scholars seeking to use these techniques …

Lexical Semantic Change detection, ie, the task of identifying words that change meaning
over time, is a very active research area, with applications in NLP, lexicography, and …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …