Abstract
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.
Abstract (translated)
联合实体和关系提取在各种应用中发挥着重要作用，特别是在知识图谱的构建中。尽管最近取得了进展，但现有的方法在两个关键方面往往存在不足：表示的丰富性和输出结构的连贯性。这些方法通常依赖于手工构建的启发式规则计算实体和关系表示，可能导致关键信息的丢失。此外，它们忽视了任务和/或数据集特定的约束，导致输出结构缺乏连贯性。在我们的工作中，我们引入了EnriCo模型，从而缓解了这些不足。首先，为了促进丰富和表现性的表示，我们的模型利用了注意机制，允许实体和关系动态确定所需的相关信息。其次，我们引入了一系列解码算法，旨在在遵守任务和数据集特定约束的情况下推断最高得分解决方案，从而促进结构和连贯的输出。与基线相比，我们的模型在Joint IE数据集上的评估表现具有竞争力。
URL
https://arxiv.org/abs/2404.12493
