Elliott Ash Profile picture
Prof @ETH Zurich: Law, Economics, and Data Science. Previously @warwickecon, @PrincetonSPIA, @columbia_econ, @ColumbiaLaw, @UTAustin. AI+Econ, Robot Judges.
May 19, 2023 6 tweets 4 min read
Q: Give an example NLP task where transformers don't give the best performance. A: Extractive summarization on legal documents!

Just posted: "Legal Extractive Summarization of U.S. Court Opinions":
arxiv.org/abs/2305.08428
(with E. Bauer, @dominsta_nlp & N. Gu, all of @ETH_en)

Repo: github.com/bauerem/legal_…

Web demo: huggingface.co/spaces/bauerem… ImageImageImage
May 18, 2023 16 tweets 16 min read
Recently posted at @cepr_org: "Text Algorithms in Economics" with @StephenEKHansen (forthcoming in ARE @AnnualReviews).

Ungated version: elliottash.com/wp-content/upl…

Companion code notebooks: github.com/sekhansen/text…

& now a 🧵 on the highlights... Image Methods covered in the "Text Algorithms" section:
1) preliminaries/ pre-processing
2) bag-of-words representation of docs
3) dimensionality reduction, e.g. topic models
4) word embeddings with local context
5) embedding sequences with attention
6) supervised learning Image
Aug 15, 2021 15 tweets 12 min read
A short thread introducing new work with @PinchOfData and @phinifa:

“Text Semantics Capture Political and Economic Narratives”

Paper: arxiv.org/abs/2108.01720
Repo: github.com/relatio-nlp/re…
Demo: colab.research.google.com/drive/1Zychi2O… Human beings are storytellers.

It’s no wonder then that social scientists are increasingly interested in narratives -- the stories we tell in fiction, politics, and life -- and how they shape beliefs, behavior, and government policies.

e.g. @RobertJShiller
Feb 7, 2021 11 tweets 7 min read
“In politics, when reason and emotion collide, emotion invariably wins.” -- Drew Westen, The Political Brain.

Interesting idea! What do the data say about that?

New working paper: "Emotion and Reason in Political Language", with @gloriagennaro.

PDF: bit.ly/gennaro-ash-em… We use computational linguistics tools ("word embeddings") to map out a dimension for emotion on one pole and cognition on another pole.