A fine-grained Chinese word segmentation and part-of-speech tagging corpus for clinical text.
Background: Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks of Chinese text processing. They are usually preliminary steps for lots of Chinese natural language processing (NLP) tasks. There have been a large number of studies on CWS and POS tagging in vario...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 19 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/9/2019 Supplement 2
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135796248&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135796248 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/9/2019 Supplement 2 vid: 19 pid: 24147 pub: BioMed Central artinfo: ui: 135796248 135796248 NLM30961602 135796248 10.1186/s12911-019-0770-7 NLM30961602 135796248 ppct: 1 formats: tig: atl: A fine-grained Chinese word segmentation and part-of-speech tagging corpus for clinical text. aug: au: Xiong, Ying Wang, Zhongmin Jiang, Dehuan Wang, Xiaolong Chen, Qingcai Xu, Hua Yan, Jun Tang, Buzhou affil: Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China sug: subj: Natural Language Processing Information Retrieval Speech China Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Short Portable Mental Status Questionnaire ab: Background: Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks of Chinese text processing. They are usually preliminary steps for lots of Chinese natural language processing (NLP) tasks. There have been a large number of studies on CWS and POS tagging in various domains, however, few studies have been proposed for CWS and POS tagging in the clinical domain as it is not easy to determine granularity of words.Methods: In this paper, we investigated CWS and POS tagging for Chinese clinical text at a fine-granularity level, and manually annotated a corpus. On the corpus, we compared two state-of-the-art methods, i.e., conditional random fields (CRF) and bidirectional long short-term memory (BiLSTM) with a CRF layer. In order to validate the plausibility of the fine-grained annotation, we further investigated the effect of CWS and POS tagging on Chinese clinical named entity recognition (NER) on another independent corpus.Results: When only CWS was considered, CRF achieved higher precision, recall and F-measure than BiLSTM-CRF. When both CWS and POS tagging were considered, CRF also gained an advantage over BiLSTM. CRF outperformed BiLSTM-CRF by 0.14% in F-measure on CWS and by 0.34% in F-measure on POS tagging. The CWS information brought a greatest improvement of 0.34% in F-measure, while the CWS&POS information brought a greatest improvement of 0.74% in F-measure.Conclusions: Our proposed fine-grained CWS and POS tagging corpus is reliable and meaningful as the output of the CWS and POS tagging systems developed on this corpus improved the performance of a Chinese clinical NER system on another independent corpus. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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