论文标题
使用上下文化表示的增强表填充的命名实体识别和关系提取
Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized Representations
论文作者
论文摘要
在这项研究中，提出了一种基于表表示的非结构化文本中提取命名实体和关系的新颖方法。通过使用上下文化的单词嵌入，所提出的方法计算实体提及和远程依赖性的表示，而没有复杂的手工特征或神经网络架构。我们还调整了张量点产品，以一次预测关系标签，而无需诉诸基于历史的预测或搜索策略。这些进步大大简化了提取指定实体和关系的模型和算法。尽管它很简单，但实验结果表明，所提出的方法的表现优于Conll04和ACE05英语数据集上的最新方法。我们还确认，当为上下文聚合提供多个句子时，所提出的方法与ACE05数据集上的最新模型实现了可比的性能。
In this study, a novel method for extracting named entities and relations from unstructured text based on the table representation is presented. By using contextualized word embeddings, the proposed method computes representations for entity mentions and long-range dependencies without complicated hand-crafted features or neural-network architectures. We also adapt a tensor dot-product to predict relation labels all at once without resorting to history-based predictions or search strategies. These advances significantly simplify the model and algorithm for the extraction of named entities and relations. Despite its simplicity, the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on the CoNLL04 and ACE05 English datasets. We also confirm that the proposed method achieves a comparable performance with the state-of-the-art NER models on the ACE05 datasets when multiple sentences are provided for context aggregation.
