EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation Extraction
This work addresses the extraction of entities and relations for applications like knowledge graphs, but it appears incremental as it builds on existing approaches with specific improvements.
The paper tackled the problem of joint entity and relation extraction by addressing limitations in representation richness and output coherence, resulting in a model that demonstrates competitive performance on Joint IE datasets.
Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs. Despite recent progress, existing approaches often fall short in two key aspects: richness of representation and coherence in output structure. These models often rely on handcrafted heuristics for computing entity and relation representations, potentially leading to loss of crucial information. Furthermore, they disregard task and/or dataset-specific constraints, resulting in output structures that lack coherence. In our work, we introduce EnriCo, which mitigates these shortcomings. Firstly, to foster rich and expressive representation, our model leverage attention mechanisms that allow both entities and relations to dynamically determine the pertinent information required for accurate extraction. Secondly, we introduce a series of decoding algorithms designed to infer the highest scoring solutions while adhering to task and dataset-specific constraints, thus promoting structured and coherent outputs. Our model demonstrates competitive performance compared to baselines when evaluated on Joint IE datasets.