Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection
Young Min Cho, Li Zhang, Chris Callison-Burch · 2022
Entity linking, the task of linking potentially ambiguous mentions in texts to corresponding knowledge-base entities, is an important component for language understanding.We address two challenge in entity linking: how to leverage wider contexts surrounding a mention, and how to deal with limited training data.We propose a fully unsupervised model called SumMC that first generates a guided summary of the contexts conditioning on the mention, and then casts the task to a multiple-choice problem where the model chooses an entity from a list of candidates.In addition to evaluating our model on existing datasets that focus on named entities, we create a new dataset that links noun phrases from WikiHow to Wikidata.We show that our SumMC model achieves stateof-the-art unsupervised performance on our new dataset and on existing datasets.