@inproceedings{sukhareva-etal-2016-crowdsourcing,
title = "Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations",
author = "Sukhareva, Maria and
Eckle-Kohler, Judith and
Habernal, Ivan and
Gurevych, Iryna",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1338",
pages = "2131--2137",
abstract = "We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license at www.ukp.tu-darmstadt.de/data/verb-relations/. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization.",
}
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%0 Conference Proceedings
%T Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations
%A Sukhareva, Maria
%A Eckle-Kohler, Judith
%A Habernal, Ivan
%A Gurevych, Iryna
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 may
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F sukhareva-etal-2016-crowdsourcing
%X We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license at www.ukp.tu-darmstadt.de/data/verb-relations/. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization.
%U https://aclanthology.org/L16-1338
%P 2131-2137
Markdown (Informal)
[Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations](https://aclanthology.org/L16-1338) (Sukhareva et al., LREC 2016)
ACL