Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018 |
| Editors | Vasile Rus, Keith Brawner |
| Publisher | AAAI press |
| Pages | 487-492 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781577357964 |
| State | Published - Jan 1 2018 |
| Event | 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018 - Melbourne, United States Duration: May 21 2018 → May 23 2018 |
Publication series
| Name | Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018 |
|---|
Conference
| Conference | 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018 |
|---|---|
| Country | United States |
| City | Melbourne |
| Period | 5/21/18 → 5/23/18 |
ASJC Scopus subject areas
- Artificial Intelligence
- Software
Cite this
Including new patterns to improve event extraction systems. / Cao, Kai; Li, Xiang; Ma, Weicheng; Grishman, Ralph.
Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018. ed. / Vasile Rus; Keith Brawner. AAAI press, 2018. p. 487-492 (Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Including new patterns to improve event extraction systems
AU - Cao, Kai
AU - Li, Xiang
AU - Ma, Weicheng
AU - Grishman, Ralph
PY - 2018/1/1
Y1 - 2018/1/1
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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85071918719&partnerID=8YFLogxK
M3 - Conference contribution
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 - Rus, Vasile
A2 - Brawner, Keith
PB - AAAI press
ER -