@inproceedings{51b0c3ec821041d7acb58fcd15c5a173,
title = "Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler",
abstract = "We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. Under the umbrella of undirected graphical theory, the proposed Neural-Hidden-CRF embedded with a hidden CRF layer models the variables of word sequence, latent ground truth sequence, and weak label sequence with the global perspective that undirected graphical models particularly enjoy. In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies. Neural-Hidden-CRF is conceptually simple and empirically powerful. It obtains new state-of-the-art results on one crowdsourcing benchmark and three weak-supervision benchmarks, including outperforming the recent advanced model CHMM by 2.80 F1 points and 2.23 F1 points in average generalization and inference performance, respectively.",
keywords = "crowdsourcing, information extraction, named entity recognition, noisy label, sequence labeling, weak supervision",
author = "Zhijun Chen and Hailong Sun and Wanhao Zhang and Chunyi Xu and Qianren Mao and Pengpeng Chen",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023 ; Conference date: 06-08-2023 Through 10-08-2023",
year = "2023",
month = aug,
day = "4",
doi = "10.1145/3580305.3599445",
language = "英语",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery ",
pages = "274--285",
booktitle = "KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
address = "美国",
}