An Evaluation Framework for Mapping News Headlines to Event Classes in a
  Knowledge Graph
Steve Fonin Mbouadeu Martin Lorenzo Ken Barker Oktie Hassanzadeh
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.