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2021 | OriginalPaper | Buchkapitel

Incorporate Lexicon into Self-training: A Distantly Supervised Chinese Medical NER

verfasst von : Zhen Gan, Zhucong Li, Baoli Zhang, Jing Wan, Yubo Chen, Kang Liu, Jun Zhao, Yafei Shi, Shengping Liu

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Medical named entity recognition (NER) tasks usually lack sufficient annotation data. Distant supervision is often used to alleviate this problem, which can quickly and automatically generate annotated training datasets through dictionaries. However, the current distantly supervised method suffers from noisy labeling due to limited coverage of the dictionary, which will cause a large number of unlabeled entities. We call this phenomenon an incomplete annotation problem. To tackle the incomplete annotation problem, we propose a novel distantly supervised method for Chinese medical NER. Specifically, we propose a high recall self-training mechanism to recall potential unlabeled entities in the distant supervision dataset. To reduce error in the high recall self-training, we propose a fine-grained lexicon enhanced scoring and ranking mechanism. Our method improves 3.2% and 5.03% compared to the baseline models on the dataset we proposed and a benchmark dataset for Chinese medical NER.

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Vorheriges Kapitel HAIN: Hierarchical Aggregation and Inference Network for Document-Level Relation Extraction

Nächstes Kapitel Diversified Paraphrase Generation with Commonsense Knowledge Graph

Code is available at https://github.com/ganzhenj/2021NLPCC.

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Titel: Incorporate Lexicon into Self-training: A Distantly Supervised Chinese Medical NER
verfasst von: Zhen Gan
Zhucong Li
Baoli Zhang
Jing Wan
Yubo Chen
Kang Liu
Jun Zhao
Yafei Shi
Shengping Liu
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-030-88479-6

Electronic ISBN: 978-3-030-88480-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-88480-2_27

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