An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph

Steve Fonin Mbouadeu, Martin Lorenzo, Ken Barker, Oktie Hassanzadeh

Abstract
Mapping ongoing news headlines to event-related classes in a rich knowledge base can be an important component in a knowledge-based event analysis and forecasting solution. In this paper, we present a methodology for creating a benchmark dataset of news headlines mapped to event classes in Wikidata, and resources for the evaluation of methods that perform the mapping. We use the dataset to study two classes of unsupervised methods for this task: 1) adaptations of classic entity linking methods, and 2) methods that treat the problem as a zero-shot text classification problem. For the first approach, we evaluate off-the-shelf entity linking systems. For the second approach, we explore a) pre-trained natural language inference (NLI) models, and b) pre-trained large generative language models. We present the results of our evaluation, lessons learned, and directions for future work. The dataset and scripts for evaluation are made publicly available.
Anthology ID:
2023.case-1.6
Volume:
Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text
Month:
sEPTEMBER
Year:
2023
Address:
Varna, Bulgaria
Editors:
Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella, Reyyan Yeniterzi, Erdem Yörük, Milena Slavcheva
Venues:
CASE | WS
SIG:
Publisher:
INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:
44–52
Language:
URL:
https://aclanthology.org/2023.case-1.6
DOI:
Bibkey:
Cite (ACL):
Steve Fonin Mbouadeu, Martin Lorenzo, Ken Barker, and Oktie Hassanzadeh. 2023. An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph. In Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, pages 44–52, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph (Mbouadeu et al., CASE-WS 2023)
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https://preview.aclanthology.org/dois-2013-emnlp/2023.case-1.6.pdf
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