In this paper, we propose an attention-based joint model enhanced with implicit entity features for the extraction task. In particular, we build our model upon the tagging scheme proposed by Zheng et al. [38]. Firstly, inspired by the work of Gao et al. [6]
基于 #203 的tagging scheme, 基于[6]
[6] Yuze Gao, Yue Zhang, and Tong Xiao. 2017. Implicit Syntactic Features for Target-dependent Sentiment Analysis. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Vol. 1. 516–524.
一句话总结：
现在的joint model没有利用好entity feature。这里视同hidden-layer来学习implicit entity feature, 并用attention来选择句子中informative part.
资源：
论文信息：
笔记：
In this paper, we propose an attention-based joint model enhanced with implicit entity features for the extraction task. In particular, we build our model upon the tagging scheme proposed by Zheng et al. [38]. Firstly, inspired by the work of Gao et al. [6]
基于 #203 的tagging scheme, 基于[6]
模型图：
结果：
接下来要看的论文：