ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation.BMC medical informatics and decision making · 2018Article
- Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016.Yearbook of medical informatics · 2017Review
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Authors and funding
4 authors.
Funding
Abstract
Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic constituents in the sentence, an effective parser, as well as an effective syntactic feature set are essential to build a practical SRL system. Our study initiates a formal evaluation and comparison of SRL performance on a clinical text corpus MiPACQ, using three state-of-the-art parsers, the Stanford parser, the Berkeley parser, and the Charniak parser. First, the original parsers trained on the open domain syntactic corpus Penn Treebank were employed. Next, those parsers were retrained on the clinical Treebank of MiPACQ for further comparison. Additionally, state-of-the-art syntactic features from open domain SRL were also examined for clinical text. Experimental results showed that retraining the parsers on clinical Treebank improved the performance significantly, with an optimal F
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28269926PMC5333340What OpenQuestion holds
Registered trials
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