Abstract
Relation extraction (RE) is an indispensable information extraction task in several disciplines. RE models typically assume that named entity recognition (NER) is already performed in a previous step by another independent model. Several recent efforts, under the theme of end-to-end RE, seek to exploit inter-task correlations by modeling both NER and RE tasks jointly. Earlier work in this area commonly reduces the task to a table-filling problem wherein an additional expensive decoding step involving beam search is applied to obtain globally consistent cell labels. In efforts that do not employ table-filling, global optimization in the form of CRFs with Viterbi decoding for the NER component is still necessary for competitive performance. We introduce a novel neural architecture utilizing the table structure, based on repeated applications of 2D convolutions for pooling local dependency and metric-based features, without the need for global optimization. We validate our model on the ADE and CoNLL04 datasets for end-to-end RE and demonstrate $\approx 1\%$ gain (in F-score) over prior best results with training and testing times that are nearly four times faster --- the latter highly advantageous for time-sensitive end user applications.
Abstract (translated)
关系提取（RE）是多学科中不可缺少的信息提取任务。重新建模通常假定在前面的步骤中，另一个独立的模型已经执行了命名实体识别（NER）。最近几项工作以端到端的RE为主题，试图通过联合建模NER和RE任务来利用任务间的相关性。在这一领域的早期工作通常会将任务减少到表格填充问题，即应用涉及波束搜索的额外昂贵解码步骤来获得全局一致的单元标签。在不使用表格填充的工作中，对NER组件使用维特比解码的CRF形式的全局优化对于竞争性能仍然是必要的。在不需要全局优化的情况下，基于二维卷积的重复应用，引入了一种新的基于表结构的神经网络结构。我们在ADE和CONLL04数据集上验证了我们的模型，以实现端到端的RE，并通过快四倍的培训和测试时间，证明了与之前的最佳结果相比，获得了大约1%的收益（F分数），后者对时间敏感的最终用户应用程序非常有利。
URL
https://arxiv.org/abs/1905.07458
