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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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International Conference on Computational Linguistics
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

34 results · 1 filter active ·
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20082018
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
34 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 34 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 34 of 34 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Automatic Acquisition of Lexical Formality
Julian Brooke, Tong Wang, Graeme Hirst
2010· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
56
citations
affunlabeled
Learning Emotion-enriched Word Representations
Ameeta Agrawal, Aijun An, Manos Papagelis
2018· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
51
citations
affunlabeled
plWordNet 3.0 - a Comprehensive Lexical-Semantic Resource.
Marek Maziarz, Maciej Piasecki, Ewa Rudnicka, Stan Śzpakowicz, Paweł Kędzia
2016· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
32
citations
affno abstractunlabeled
Towards Automatic Topical Question Generation
Yllias Chali, Sadid A. Hasan
2012· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Scaling up Analogical Learning
Philippe Langlais, François Yvon
2008· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Cyberbullying Intervention Based on Convolutional Neural Networks
Qianjia Huang, Diana Inkpen, Jianhong Zhang, David Van Bruwaene
2018· article· en· International Conference on Computational Linguistics· Psychology
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
11
citations
affunlabeled
An interactive system for exploring community question answering forums
Enamul Hoque, Shafiq Joty, Lluı́s Màrquez, Alberto Barrón‐Cedeño, Giovanni Da San Martino, Alessandro Moschitti +3 more
2016· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Lexfom: a lexical functions ontology model
Alexsandro Fonseca, Fatiha Sadat, François Lareau
2016· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Reproducing and Regularizing the SCRN Model
Olzhas Kabdolov, Zhenisbek Assylbekov, Rustem Takhanov
2018· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About