Extracting Fine-Grained Economic Events from Business News.

I will be presenting new work on economic event extraction at the FNP-FNS workshop @ COLING2020. TL;DR: We created a dataset and system that automatically gets “what, who, when, how much, and why” is happening from economic news text. Turns out this is quite a difficult task, even with using the latest techniques in deep learning!

You can view my presentation here:

Read the paper at this link.

Based on a recently developed fine-grained event extraction dataset for the economic domain,we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art neural model for event extraction performs on the trigger and argument identification andclassification. While F1-scores of above 50% are obtained on the task of trigger identification,we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have alarge degree of lexico-semantic and contextual overlap.