Papers › From Heuristics to Language Models: A Journey Through the Universe of Semantic Table...
From Heuristics to Language Models: A Journey Through the Universe of Semantic Table Interpretation with DAGOBAH
Viet-Phi Huynh, Yoan Chabot, Thomas Labbé, Jixiong Liu and Raphaël Troncy
This paper presents DAGOBAH SL 2022, a semantic table interpretation system that has been continuously improved over the last four years when participating in the SemTab challenge. This year, we have improved the lookup coverage using external resources and we have integrated language models for better understanding the table headers. We have also implemented various system optimizations that lead to a reduction in execution time of about 30%. In this paper, we also show the relevance of using deep learning-based approaches for resolving certain ambiguities and we discuss the limitations of existing corpora and systems for maturing further this research field.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cell Entity Annotation | ToughTables-WD | DAGOBAH | F1 (%) | 94.5 | #1 of 5 | Archive leaderboard | report |
| Column Type Annotation | ToughTables-WD | DAGOBAH | F1 (%) | 40.9 | #5 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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