Abstract
This article presents a two-step methodology to annotate temporally anchored spatial knowledge on top of OntoNotes. We first generate potential knowledge using semantic roles or syntactic dependencies and then crowdsource annotations to validate the potential knowledge. The resulting annotations indicate how long entities are or are not located somewhere and temporally anchor this spatial information. We present an in-depth corpus analysis comparing the spatial knowledge generated by manipulating roles or dependencies. Experiments show that working with syntactic dependencies instead of semantic roles allows us to generate more potential entity-related spatial knowledge and obtain better results in a realistic scenario, that is, with predicted linguistic information.
Original language | English (US) |
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Pages (from-to) | 519-543 |
Number of pages | 25 |
Journal | Natural Language Engineering |
Volume | 27 |
Issue number | 5 |
DOIs | |
State | Published - Sep 2021 |
Externally published | Yes |
Keywords
- Information extraction
- Spatial knowledge
ASJC Scopus subject areas
- Software
- Language and Linguistics
- Linguistics and Language
- Artificial Intelligence