DISCIE -- Discriminative Closed Information Extraction
This work addresses the problem of accurate and efficient information extraction for applications dealing with large-scale data, though it appears incremental as it builds on existing discriminative and generative approaches.
The paper tackles closed information extraction by introducing a discriminative method that incorporates type and entity-specific information, achieving superior performance compared to state-of-the-art end-to-end generative models, especially for long-tail relations and large-scale scenarios with millions of entities and hundreds of relations.
This paper introduces a novel method for closed information extraction. The method employs a discriminative approach that incorporates type and entity-specific information to improve relation extraction accuracy, particularly benefiting long-tail relations. Notably, this method demonstrates superior performance compared to state-of-the-art end-to-end generative models. This is especially evident for the problem of large-scale closed information extraction where we are confronted with millions of entities and hundreds of relations. Furthermore, we emphasize the efficiency aspect by leveraging smaller models. In particular, the integration of type-information proves instrumental in achieving performance levels on par with or surpassing those of a larger generative model. This advancement holds promise for more accurate and efficient information extraction techniques.