- Build theory-driven frameworks (SIBling & LSC-Eval) to model semantic change in societally relevant concepts
- Supervisors: Nick Haslam & Kat Vylomova
🌐 naomibaes.github.io
This general-purpose framework evaluates methods for assessing dimensions of lexical semantic change using LLM-generated synthetic data to test how well measures detect changes.
📍MON @ 6 pm, Hall 4/5
Paper: aclanthology.org/2025.finding...
Details: www.changeiskey.org/event/2026-e...
#NLProc #EACL2026
Details: www.changeiskey.org/event/2026-e...
#NLProc #EACL2026
Co-located with EACL 2026, Rabat, Morocco & online (March 24–29, 2026).
Submissions due Dec 19 2025 (Direct) / Jan 2 2026 (ARR).
🌐 www.changeiskey.org/event/2026-e...
Co-located with EACL 2026, Rabat, Morocco & online (March 24–29, 2026).
Submissions due Dec 19 2025 (Direct) / Jan 2 2026 (ARR).
🌐 www.changeiskey.org/event/2026-e...
And join us for the NLP4Democracy workshop on Friday!
sites.google.com/andrew.cmu.e...
#NLP #NLProc #LLM #ComputationalSocialScience
And join us for the NLP4Democracy workshop on Friday!
sites.google.com/andrew.cmu.e...
#NLP #NLProc #LLM #ComputationalSocialScience
1 causality: how to pose and identify effects
2 regression: as a tool for inference, not prediction
3 benchmarks: as measurements, not trophies
4 experiments: with rigor, power, and ethics
1 causality: how to pose and identify effects
2 regression: as a tool for inference, not prediction
3 benchmarks: as measurements, not trophies
4 experiments: with rigor, power, and ethics
Shared 2 frameworks - SIBling (Sentiment, Intensity, Breadth) + LSC-Eval - applied to mental health concepts.
Slides: www.slideshare.net/slideshow/di...
Shared 2 frameworks - SIBling (Sentiment, Intensity, Breadth) + LSC-Eval - applied to mental health concepts.
Slides: www.slideshare.net/slideshow/di...
We extend the BLEnD Benchmark to >30 language-culture pairs. [Our task is Junior-friendly, with live Q&A & tutorials.] 1/
We extend the BLEnD Benchmark to >30 language-culture pairs. [Our task is Junior-friendly, with live Q&A & tutorials.] 1/
#NLP #NLProc
#NLP #NLProc
I presented:
🔹SIBling – models semantic change (Sentiment, Intensity, Breadth)
🔹LSC-Eval – synthetic benchmarks for evaluating change methods
Slides: www.slideshare.net/slideshow/di...
I presented:
🔹SIBling – models semantic change (Sentiment, Intensity, Breadth)
🔹LSC-Eval – synthetic benchmarks for evaluating change methods
Slides: www.slideshare.net/slideshow/di...
Link: quillette.com/2025/08/21/t...
Link: quillette.com/2025/08/21/t...
See proceedings here: gu-clasp.github.io/LARP/program...
See proceedings here: gu-clasp.github.io/LARP/program...
I’ll show how SIBling (Sentiment, Intensity, Breadth) helps track semantic change in mental health terms.
Link: www.gu.se/en/event/cha...
I’ll show how SIBling (Sentiment, Intensity, Breadth) helps track semantic change in mental health terms.
Link: www.gu.se/en/event/cha...
1/n
1/n
www.youtube.com/watch?v=NXYg...
www.youtube.com/watch?v=NXYg...
YouTube: youtu.be/sbY40h-1gKY
Book: www.cambridge.org/core/element...
YouTube: youtu.be/sbY40h-1gKY
Podcast: bit.ly/4hLHjvL
YouTube: youtu.be/sbY40h-1gKY
Book: www.cambridge.org/core/element...
🎧Find it here go.unimelb.edu.au/7axe
🎧Find it here go.unimelb.edu.au/7axe
#NLP #NLProc
#NLP #NLProc
⬇️ Read more about our findings in the post below!
aclanthology.org/2025.woah-1....
I built classification models to detect objectifying language and found: 10% of comments refer to appearance, 3% are objectifying. Models struggle with this task, with #RoBERTa outperforming GPT-4.
⬇️ Read more about our findings in the post below!
aclanthology.org/2025.woah-1....
Big thanks to all the team members!
Big thanks to all the team members!