@inproceedings{0931027c8ec74e2383980d0a1039714e,
title = "Knowledge Enhanced Target-Aware Stance Detection on Tweets",
abstract = "Stance detection aims to determine the stance of a text towards a given target. Different from aspect-level sentiment classification, the target may not appear in the text. While existing models have achieved great success in this task using deep neural networks, their performances still drop sharply on cases where targets are not directly mentioned in texts, even with {\textquoteleft}target-aware{\textquoteright} structures. We argue that the nonalignment between targets and potentially opinioned terms in texts causes such failure and this could be remedied with external knowledge as a bridge. To this end, we propose RelNet, which leverages multiple external knowledge bases as bridges to explicitly link potentially opinioned terms in texts to targets of interest. Experiments on the well-adopted SemEval 2016 task 6 dataset demonstrate the effectiveness of the proposed model, especially on the subset where targets do not appear in texts.",
keywords = "External knowledge, Stance detection, Target-awareness",
author = "Xin Zhang and Jianhua Yuan and Yanyan Zhao and Bing Qin",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; 6th China Conference on Knowledge Graph and Semantic Computing, CCKS 2021 ; Conference date: 04-11-2021 Through 07-11-2021",
year = "2021",
doi = "10.1007/978-981-16-6471-7\_13",
language = "英语",
isbn = "9789811664700",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "171--184",
editor = "Bing Qin and Zhi Jin and Haofen Wang and Jeff Pan and Yongbin Liu and Bo An",
booktitle = "Knowledge Graph and Semantic Computing",
address = "德国",
}