Shaodian Zhang


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Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
Zhenghui Wang | Yanru Qu | Liheng Chen | Jian Shen | Weinan Zhang | Shaodian Zhang | Yimei Gao | Gen Gu | Ken Chen | Yong Yu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER tasks.

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Adaptive Multi-Task Transfer Learning for Chinese Word Segmentation in Medical Text
Junjie Xing | Kenny Zhu | Shaodian Zhang
Proceedings of the 27th International Conference on Computational Linguistics

Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop when dealing with domain text, especially for a domain with lots of special terms and diverse writing styles, such as the biomedical domain. However, building domain-specific CWS requires extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant knowledge from high resource to low resource domains. Extensive experiments show that our model achieves consistently higher accuracy than the single-task CWS and other transfer learning baselines, especially when there is a large disparity between source and target domains.


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Recognizing Named Entities in Tweets
Xiaohua Liu | Shaodian Zhang | Furu Wei | Ming Zhou
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Hedge Detection and Scope Finding by Sequence Labeling with Procedural Feature Selection
Shaodian Zhang | Hai Zhao | Guodong Zhou | Bao-Liang Lu
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task