Annotating Temporally-Anchored Spatial Knowledge on Top of OntoNotes Semantic Roles

Alakananda Vempala, Eduardo Blanco


Abstract
This paper presents a two-step methodology to annotate spatial knowledge on top of OntoNotes semantic roles. First, we manipulate semantic roles to automatically generate potential additional spatial knowledge. Second, we crowdsource annotations with Amazon Mechanical Turk to either validate or discard the potential additional spatial knowledge. The resulting annotations indicate whether entities are or are not located somewhere with a degree of certainty, and temporally anchor this spatial information. Crowdsourcing experiments show that the additional spatial knowledge is ubiquitous and intuitive to humans, and experimental results show that it can be inferred automatically using standard supervised machine learning techniques.
Anthology ID:
L16-1604
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3814–3821
Language:
URL:
https://www.aclweb.org/anthology/L16-1604
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/L16-1604.pdf