Including new patterns to improve event extraction systems

Kai Cao, Xiang Li, Weicheng Ma, Ralph Grishman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018
EditorsVasile Rus, Keith Brawner
PublisherAAAI press
Pages487-492
Number of pages6
ISBN (Electronic)9781577357964
StatePublished - Jan 1 2018
Event31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018 - Melbourne, United States
Duration: May 21 2018May 23 2018

Publication series

NameProceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018

Conference

Conference31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018
CountryUnited States
CityMelbourne
Period5/21/185/23/18

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Cao, K., Li, X., Ma, W., & Grishman, R. (2018). Including new patterns to improve event extraction systems. In V. Rus, & K. Brawner (Eds.), Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018 (pp. 487-492). (Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018). AAAI press.

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 proceedingConference contribution

Cao, K, Li, X, Ma, W & Grishman, R 2018, Including new patterns to improve event extraction systems. in V Rus & K Brawner (eds), Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018. Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018, AAAI press, pp. 487-492, 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018, Melbourne, United States, 5/21/18.
Cao K, Li X, Ma W, Grishman R. Including new patterns to improve event extraction systems. In Rus V, Brawner K, editors, Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018. AAAI press. 2018. p. 487-492. (Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018).
Cao, Kai ; Li, Xiang ; Ma, Weicheng ; Grishman, Ralph. / Including new patterns to improve event extraction systems. Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018. editor / Vasile Rus ; Keith Brawner. AAAI press, 2018. pp. 487-492 (Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018).
@inproceedings{fa17924f2562430f8e3110d55bc047f6,
title = "Including new patterns to improve event extraction systems",
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.",
author = "Kai Cao and Xiang Li and Weicheng Ma and Ralph Grishman",
year = "2018",
month = "1",
day = "1",
language = "English (US)",
series = "Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018",
publisher = "AAAI press",
pages = "487--492",
editor = "Vasile Rus and Keith Brawner",
booktitle = "Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018",

}

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.

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

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 -