Papers by Leopold Fuchs

2 papers
Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction (2023.acl-demo)

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Challenge: ZSL is a machine learning field that uses textual descriptions of entities or relations to perform tasks that are not seen during training.
Approach: They propose a framework that allows researchers to compare state-of-the-art ZSL methods with standard benchmark datasets.
Outcome: The proposed framework compares state-of-the-art methods with benchmark datasets and provides APIs for production under the standard SpaCy NLP pipeline.
Description Boosting for Zero-Shot Entity and Relation Classification (2024.findings-acl)

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Challenge: Named Entity Recognition and Relation Extraction (RE) methods are expensive and require domain experts for data acquisition and labeling.
Approach: They propose a strategy for generating variations of an initial description, a heuristic for ranking them and an ensemble method capable of boosting the predictions of zero-shot models.
Outcome: The proposed method outperforms existing approaches and achieves new SOTA results on four different entity and relation classification datasets.

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