计算机科学
关系抽取
接头(建筑物)
变压器
自然语言处理
人工智能
关系(数据库)
自然语言
编码器
统一模型
自然语言理解
情报检索
信息抽取
数据挖掘
建筑工程
物理
量子力学
电压
气象学
工程类
操作系统
作者
Haixin Tan,Zhihao Yang,Zeyuan Ding,Zhijun Wang,Ling Luo,Lei Wang,Yin Zhang,Wei Liu,Hongfei Lin,Jian Wang
标识
DOI:10.1109/bibm58861.2023.10385642
摘要
Automatic extraction of entities and their relations from unstructured literature to form structured triples is essential for biomedical knowledge construction. Although most existing joint methods have effectively addressed some challenging problems in the biomedical corpora, i.e., the prevalent overlapping issue, they still suffer from a lack of consideration for the intrinsic correlations between entities and relations, as well as low computational efficiency. In this paper, we present a joint entity and relation extraction model with unified interaction maps. Specifically, we concatenate all relations in the natural language form with the input text to integrate the semantic information of relations through a deep Transformer-based encoder. In addition, we apply unified interaction maps to capture the correlations, which can naturally handle the overlapping issue. Extensive experiments on the CHEMPROT and DDIExtraction2013 datasets demonstrate the effectiveness of our model, achieving the state-of-the-art performance with higher efficiency.
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