Difference between revisions of "Temporal Scoping of Relational Facts based on Wikipedia Data"
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| + | | 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. | ||
Revision as of 22:40, 12 August 2019
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.
