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20202023
most citedTimeLMs: Diachronic Language Models from Twitter

27 citations · 86 across the 8 of their papers we have counts for

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9 papers · 1 filter

cs.CL20231 cited

SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri +5

Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks. This fragmented landscape makes it hard for the…

cs.CL20233 cited

Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter

Daniel Loureiro, Kiamehr Rezaee, Talayeh Riahi +4

This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models.…

cs.CL20225 cited

Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts

Asahi Ushio, Leonardo Neves, Vitor Silva +2

Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-format…

cs.CL202218 cited

Twitter Topic Classification

Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados +3

Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A…

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…