A gated piecewise CNN with entity-aware enhancement for distantly supervised relation extraction

作者：

Highlights：

• We propose an entity-aware enhanced word representation with rich context, which enables the downstream modules to learn robust semantic features.

• We combine the global gate structure and PCNN to better capture the global and local features of the sentence.

• We introduce a gate mechanism after the max-pooling layer of PCNN model, which assigns different weights to the three segments and highlights the effect of crucial segments.

• Our model is evaluated on the widely used benchmark dataset and outperforms most of the stateof-the-art methods.

摘要

•We propose an entity-aware enhanced word representation with rich context, which enables the downstream modules to learn robust semantic features.•We combine the global gate structure and PCNN to better capture the global and local features of the sentence.•We introduce a gate mechanism after the max-pooling layer of PCNN model, which assigns different weights to the three segments and highlights the effect of crucial segments.•Our model is evaluated on the widely used benchmark dataset and outperforms most of the stateof-the-art methods.

论文关键词：Distant supervision,Relation extraction,PCNN,Multi-head self-attention,Gate mechanism

论文评审过程：Received 29 March 2020, Revised 15 July 2020, Accepted 12 August 2020, Available online 18 August 2020, Version of Record 20 October 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2020.102373