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20162022
most citedTimeLMs: Diachronic Language Models from Twitter

27 citations · 56 across the 11 of their papers we have counts for

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21 papers · 1 filter

cs.CL202213 cited

TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media

Daniel Loureiro, Aminette D'Souza, Areej Nasser Muhajab +6

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 chall…

cs.CL202227 cited

TimeLMs: Diachronic Language Models from Twitter

Daniel Loureiro, Francesco Barbieri, Leonardo Neves +2

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 specializ…

cs.CL2022

Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction

Devansh Jain, Luis Espinosa Anke

In this paper, we analyze zero-shot taxonomy learning methods which are based on distilling knowledge from language models via prompting and sentence scoring. We show that, despite…

cs.CL2021

Overview of ADoBo 2021: Automatic Detection of Unassimilated Borrowings in the Spanish Press

Elena Álvarez Mellado, Luis Espinosa Anke, Julio Gonzalo Arroyo +2

This paper summarizes the main findings of the ADoBo 2021 shared task, proposed in the context of IberLef 2021. In this task, we invited participants to detect lexical borrowings (…

cs.CL2021

Deriving Disinformation Insights from Geolocalized Twitter Callouts

David Tuxworth, Dimosthenis Antypas, Luis Espinosa-Anke +3

This paper demonstrates a two-stage method for deriving insights from social media data relating to disinformation by applying a combination of geospatial classification and embedd…

cs.CL2021

Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection

Yixiao Wang, Zied Bouraoui, Luis Espinosa Anke +1

One of the long-standing challenges in lexical semantics consists in learning representations of words which reflect their semantic properties. The remarkable success of word embed…