Composition-driven symptom phrase recognition for Chinese medical consultation corpora
文献类型：期刊论文
| 作者 | Gu,Xuan1,2; Sun,Zhengya1,2 ; Zhang,Wensheng1,2
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| 刊名 | BMC Medical Informatics and Decision Making
 |
| 出版日期 | 2021-12-27 |
| 卷号 | 21期号:1 |
| 关键词 | Symptom phrase recognition Named entity recognition Medical consultation Composition driven |
| DOI | 10.1186/s12911-021-01716-2 |
| 通讯作者 | Sun,Zhengya(zhengya.sun@ia.ac.cn) |
| 英文摘要 | AbstractBackgroundSymptom phrase recognition is essential to improve the use of unstructured medical consultation corpora for the development of automated question answering systems. A majority of previous works typically require enough manually annotated training data or as complete a symptom dictionary as possible. However, when applied to real scenarios, they will face a dilemma due to the scarcity of the annotated textual resources and the diversity of the spoken language expressions.MethodsIn this paper, we propose a composition-driven method to recognize the symptom phrases from Chinese medical consultation corpora without any annotations. The basic idea is to directly learn models that capture the composition, i.e., the arrangement of the symptom components (semantic units of words). We introduce an automatic annotation strategy for the standard symptom phrases which are collected from multiple data sources. In particular, we combine the position information and the interaction scores between symptom components to characterize the symptom phrases. Equipped with such models, we are allowed to robustly extract symptom phrases that are not seen before.ResultsWithout any manual annotations, our method achieves strong positive results on symptom phrase recognition tasks. Experiments also show that our method enjoys great potential with access to plenty of corpora.ConclusionsCompositionality offers a feasible solution for extracting information from unstructured free text with scarce labels. |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/S12911-021-01716-2 |
| 出版者 | BioMed Central |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46757]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Sun,Zhengya |
| 作者单位 | 1.Chinese Academy of Sciences; Institute of Automation 2.University of Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Gu,Xuan,Sun,Zhengya,Zhang,Wensheng. Composition-driven symptom phrase recognition for Chinese medical consultation corpora[J]. BMC Medical Informatics and Decision Making,2021,21(1). |
| APA | Gu,Xuan,Sun,Zhengya,&Zhang,Wensheng.(2021).Composition-driven symptom phrase recognition for Chinese medical consultation corpora.BMC Medical Informatics and Decision Making,21(1). |
| MLA | Gu,Xuan,et al."Composition-driven symptom phrase recognition for Chinese medical consultation corpora".BMC Medical Informatics and Decision Making 21.1(2021). |
入库方式： OAI收割
来源：自动化研究所
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