Towards Temporal Scoping of Relational Facts based on Wikipedia Data
Avirup Sil, Silviu-Petru Cucerzan · 2014
Most previous work in informationextraction 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 automatically bootstrapped from Wikipedia sentences that contain the main entity of a page and slot-fillers extracted from the corresponding infoboxes.This proposed system achieves state-of-the-art results on 6 out of 7 relations on the benchmark Text Analysis Conference 2013 dataset for temporal slot filling (TSF), and outperforms the next best system in the TAC 2013 evaluation by more than 10 points.