Papers by Sam Brody

2 papers
Towards Realistic Few-Shot Relation Extraction (2021.emnlp-main)

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Challenge: Recent studies have shown that few-shot relation classification models can be used to extract any relation of interest from a collection of text with only a few example instances.
Approach: They propose to modify the training routine to encourage models to better discriminate between relations involving similar entity types.
Outcome: The proposed models outperform human models on relation extraction tasks while relying on entity type information.
Enhanced Distant Supervision with State-Change Information for Relation Extraction (2022.lrec-1)

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Challenge: Existing methods for enhancing distant supervision with state-change information for relation extraction are limited.
Approach: They propose a method for enhancing distant supervision with state-change information for relation extraction by adding temporal information to a curation dataset.
Outcome: The proposed method reduces noise when used for static relation extraction and can be used to train a relation-extraction system that detects a change of state in relations.

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