SemRelData ― Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines

Darina Benikova, Chris Biemann

Abstract
Semantic relations play an important role in linguistic knowledge representation. Although their role is relevant in the context of written text, there is no approach or dataset that makes use of contextuality of classic semantic relations beyond the boundary of one sentence. We present the SemRelData dataset that contains annotations of semantic relations between nominals in the context of one paragraph. To be able to analyse the universality of this context notion, the annotation was performed on a multi-lingual and multi-genre corpus. To evaluate the dataset, it is compared to large, manually created knowledge resources in the respective languages. The comparison shows that knowledge bases not only have coverage gaps; they also do not account for semantic relations that are manifested in particular contexts only, yet still play an important role for text cohesion.
Anthology ID:
L16-1656
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4154–4161
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/L16-1656/
DOI:
Bibkey:
Cite (ACL):
Darina Benikova and Chris Biemann. 2016. SemRelData ― Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 4154–4161, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
SemRelData ― Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines (Benikova & Biemann, LREC 2016)
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https://preview.aclanthology.org/icon-24-ingestion/L16-1656.pdf
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