TY - GEN
T1 - Including new patterns to improve event extraction systems
AU - Cao, Kai
AU - Li, Xiang
AU - Ma, Weicheng
AU - Grishman, Ralph
PY - 2018
Y1 - 2018
N2 - Event Extraction (EE) is a challenging Information Extraction task which aims to discover event triggers of specific types along with their arguments. Most recent research on Event Extraction relies on pattern-based or feature-based approaches, trained on annotated corpora, to recognize combinations of event triggers, arguments, and other contextual information. However, as the event instances in the ACE corpus are not evenly distributed, some frequent expressions involving ACE event triggers do not appear in the training data, adversely affecting the performance. In this paper, we demonstrate the effectiveness of systematically importing expert-level patterns from TABARI to boost EE performance. The experimental results demonstrate that our pattem-based system with the expanded patterns can achieve 69.8% (with 1.9% absolute improvement) F-measure over the baseline, an advance over current state-of-the-art systems.
AB - Event Extraction (EE) is a challenging Information Extraction task which aims to discover event triggers of specific types along with their arguments. Most recent research on Event Extraction relies on pattern-based or feature-based approaches, trained on annotated corpora, to recognize combinations of event triggers, arguments, and other contextual information. However, as the event instances in the ACE corpus are not evenly distributed, some frequent expressions involving ACE event triggers do not appear in the training data, adversely affecting the performance. In this paper, we demonstrate the effectiveness of systematically importing expert-level patterns from TABARI to boost EE performance. The experimental results demonstrate that our pattem-based system with the expanded patterns can achieve 69.8% (with 1.9% absolute improvement) F-measure over the baseline, an advance over current state-of-the-art systems.
UR - http://www.scopus.com/inward/record.url?scp=85071918719&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071918719&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85071918719
T3 - Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018
SP - 487
EP - 492
BT - Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018
A2 - Brawner, Keith
A2 - Rus, Vasile
PB - AAAI press
T2 - 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018
Y2 - 21 May 2018 through 23 May 2018
ER -