Papers › Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality

Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality

1 Apr 2014EACL 2014 4archive 2025-07-28

Jey Han Lau, David Newman, Timothy Baldwin

PaperPDFCode

Code

jhlau/topic_interpretability officialmentioned in paper report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Machine TranslationReading ComprehensionTopic Models

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections