A Two-step Approach for Handling Zero-Cardinality in Relation Extraction
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Relation tuple extraction from text is an important task for building knowledge bases. Recently, joint entity and relation extraction models have achieved very high F1 scores in this task. However, the experimental settings used by these models are restrictive and the datasets used in the experiments are not realistic. They do not include sentences with zero tuples (zero-cardinality). In this paper, we evaluate the state-of-the-art joint entity and relation extraction models in a more realistic setting. We include sentences that do not contain any tuples in our experiments. Our experiments show that there is significant drop (∼ 10-15% in one dataset and ∼ 6-14% in another dataset) in their F1 score in this setting. We also propose a two-step modeling using a simple BERT-based classifier that leads to improvement in the overall performance of these models in this realistic experimental setup.
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