Paper: Towards Temporal Scoping of Relational Facts based on Wikipedia Data

ACL ID W14-1612
Title Towards Temporal Scoping of Relational Facts based on Wikipedia Data
Venue International Conference on Computational Natural Language Learning
Session
Year 2014
Authors

Most previous work in information extraction from text has focused on named-entity recognition, entity linking, and relation extraction. Less attention has been paid given to extracting the temporal scope for relations between named entities; for example, the relation president-Of(John F. Kennedy, USA) is true only in the time-frame (January 20, 1961 - November 22, 1963). In this paper we present a system for temporal scoping of relational facts, which is trained on distant supervision based on the largest semi-structured resource available: Wikipedia. The system employs language models consisting of patterns automat- ically bootstrapped from Wikipedia sentences that contain the main entity of a page and slot-fillers extracted from the corresponding infoboxes. This proposed system achieves...