@inproceedings{03d8c3c474f6407a96e63c3a6ca58245,
title = "Enriching BERT With Knowledge Graph Embedding For Industry Classification",
abstract = "Industry classification for startup companies is meaningful not only to navigate investment strategies but also to find potential competitors. It is essentially a challenging domain-specific text classification task. Due to the lack of such dataset, in this paper, we first construct a dataset for industry classification based on the companies listed on the Chinese National Equities Exchange and Quotations (NEEQ), which consists of 17, 604 annual business reports and their corresponding industry labels. Second, we introduce a novel Knowledge Graph Enriched BERT model (KGEB), which can understand a domain-specific text by enhancing the word representation with external knowledge and can take full use of the local knowledge graph without pre-training. Experimental results show the promising performance of the proposed model and demonstrate its effectiveness for tackling the domain-specific classification task.",
keywords = "Graph convolutional network, Industry classification, Knowledge graph",
author = "Shiyue Wang and Youcheng Pan and Zhenran Xu and Baotian Hu and Xiaolong Wang",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",
year = "2021",
doi = "10.1007/978-3-030-92310-5\_82",
language = "英语",
isbn = "9783030923099",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "709--717",
editor = "Teddy Mantoro and Minho Lee and Ayu, \{Media Anugerah\} and Wong, \{Kok Wai\} and Hidayanto, \{Achmad Nizar\}",
booktitle = "Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings",
address = "德国",
}