📄 中文摘要
BERT模型被应用于构建关系抽取和语义角色标注工具。这些模型能够识别句子中词语间的关联（关系抽取）以及句子中各成分的角色（语义角色）。令人惊讶的是，这些模型在不依赖额外标签或树结构等特征的情况下，依然能取得出色的性能。模型通过学习原始文本中的模式，像人类读者一样理解并识别关系和标注角色。测试结果表明，其性能达到或超越了许多传统复杂方法，达到了最先进水平。
用于关系抽取和语义角色标注的简化BERT模型
📄 English Summary
Simple BERT Models for Relation Extraction and Semantic Role Labeling
BERT models have been successfully employed to develop tools for relation extraction and semantic role labeling. These models are designed to identify connections between words within a sentence, a task known as relation extraction, and to pinpoint the roles played by different elements in a sentence, referred to as semantic roles. A significant finding is that these models achieve strong performance without the need for additional features such as tags or tree structures. This indicates their ability to learn effectively from raw text, discerning patterns and subsequently identifying relations and labeling roles with an accuracy comparable to human understanding. Performance evaluations demonstrate that these BERT-based models meet or even exceed the capabilities of many older, more complex methodologies, establishing a new state-of-the-art in these natural language processing tasks.