At this week’s lab meeting, Hassan will discuss his work on entity linking that was recently submitted to EMNLP:
Entity linking aims to create structured data by connecting text spans to an ontology or knowledge source. In this study, we revisit framing entity linking as token classification, where each input token is classified as an entity and predictions are aggregated. We propose novel ideas, including a context-sensitive prediction aggregation strategy, reducing the model’s output vocabulary, and addressing training and inference tokenization mismatch. Our state-of-the-art approach surpasses existing methods in accuracy, computational efficiency, and inference speed, as demonstrated through experiments on the widely used AIDA benchmark dataset for entity linking to Wikipedia.
Wednesday, 5 July at 12pm
This will be a hybrid meeting at TASC1 9408. The zoom link will be posted on zulip.
