When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context

We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or en tity mentioned in text. Contextualizing infor mation extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our approach uses a high-quality curated dataset of time and loca tion annotations in a corpus of epidemiology papers to train an encoder-decoder architecture We also explored the use of data augmentation techniques during training. Our findings sug gest that a relatively small fine-tuned encoder decoder model performs better than out-of-the box LLMs and semantic role labeling parsers to accurate predict the relevant scenario infor mation of a particular entity or event.