Writer's uncertainty identification in scientific biomedical articles: a tool for automatic if-clause tagging.
In a previous study, we manually identified seven categories (verbs, non-verbs, modal verbs in the simple present, modal verbs in the conditional mood, if, uncertain questions, and epistemic future) of Uncertainty Markers (UMs) in a corpus of 80 articles from the British Medical Journal randomly sam...
| Publicado en: | Language Resources & Evaluation Vol. 54; no. 4; pp. 1161 - 1182 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Dec2020
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=146751851&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 146751851 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2020 vid: 54 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 146751851 10.1007/s10579-020-09491-8 ppf: 1161 ppct: 21 formats: fmt: @attributes: type: P size: 444KB tig: atl: Writer's uncertainty identification in scientific biomedical articles: a tool for automatic if-clause tagging. aug: au: Omero, Paolo Valotto, Massimiliano Bellana, Riccardo Bongelli, Ramona Riccioni, Ilaria Zuczkowski, Andrzej Tasso, Carlo affil: University of Udine, Udine, Italy University of Macerata, Macerata, Italy su: Supervised learning Uncertainty sug: subj: Supervised learning Uncertainty keyword: Automatic if clause tagging Epistemic stance Rule-based approach Scientific biomedical articles SVM approach Uncertainty markers ab: In a previous study, we manually identified seven categories (verbs, non-verbs, modal verbs in the simple present, modal verbs in the conditional mood, if, uncertain questions, and epistemic future) of Uncertainty Markers (UMs) in a corpus of 80 articles from the British Medical Journal randomly sampled from a 167-year period (1840–2007). The UMs detected on the base of an epistemic stance approach were those referring only to the authors of the articles and only in the present. We also performed preliminary experiments to assess the manual annotated corpus and to establish a baseline for the UMs automatic detection. The results of the experiments showed that most UMs could be recognized with good accuracy, except for the if-category, which includes four subcategories: if-clauses in a narrow sense; if-less clauses; as if/as though; if and whether introducing embedded questions. The unsatisfactory results concerning the if-category were probably due to both its complexity and the inadequacy of the detection rules, which were only lexical, not grammatical. In the current article, we describe a different approach, which combines grammatical and syntactic rules. The performed experiments show that the identification of uncertainty in the if-category has been largely double improved compared to our previous results. The complex overall process of uncertainty detection can greatly profit from a hybrid approach which should combine supervised Machine learning techniques with a knowledge-based approach constituted by a rule-based inference engine devoted to the if-clause case and designed on the basis of the above mentioned epistemic stance approach. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2020. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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