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Title: Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection
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 state-of-the-art unsupervised performance on our new dataset and on exiting datasets.  more » « less
Award ID(s):
1928474
NSF-PAR ID:
10390706
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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