Difference between revisions of "Temporal Scoping of Relational Facts based on Wikipedia Data"

From Wikipedia Quality
Jump to: navigation, search
(+ wikilinks)
(+ infobox)
 
Line 1: Line 1:
 +
{{Infobox work
 +
| title = Temporal Scoping of Relational Facts based on Wikipedia Data
 +
| date = 2014
 +
| authors = [[Avirup Sil]]<br />[[Silviu Cucerzan]]
 +
| link = http://www.aclweb.org/anthology/W/W14/W14-1612.pdf
 +
}}
 
'''Temporal Scoping of Relational Facts based on Wikipedia Data''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Avirup Sil]] and [[Silviu Cucerzan]].
 
'''Temporal Scoping of Relational Facts based on Wikipedia Data''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Avirup Sil]] and [[Silviu Cucerzan]].
  
 
== Overview ==
 
== Overview ==
 
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 authors 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.
 
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 authors 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.

Latest revision as of 22:40, 12 August 2019


Temporal Scoping of Relational Facts based on Wikipedia Data
Authors
Avirup Sil
Silviu Cucerzan
Publication date
2014
Links
Original

Temporal Scoping of Relational Facts based on Wikipedia Data - scientific work related to Wikipedia quality published in 2014, written by Avirup Sil and Silviu Cucerzan.

Overview

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 authors 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.