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Home Ricerca News Ricerca Articolo: Writer’s uncertainty identification in scientific biomedical articles: a tool for automatic if-clause tagging. Language Resources and Evaluation. Omero, P., Valotto, M., Bellana, R., Bongelli, R., Riccioni, I., Zuczkowski, A., & Tasso, C. (2020)

Articolo: Writer’s uncertainty identification in scientific biomedical articles: a tool for automatic if-clause tagging. Language Resources and Evaluation. Omero, P., Valotto, M., Bellana, R., Bongelli, R., Riccioni, I., Zuczkowski, A., & Tasso, C. (2020)

Prof.ssa Ramona Bongelli

Prof.ssa Ramona Bongelli

Language Resources and Evaluation is a hybrid open access journal, edited by Springer, "devoted to the acquisition, creation, annotation, and use of language resources, together with methods for evaluation of resources, technologies, and applications".

Journal IF 1.014 (2019)

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.

Although much research has been carried out on uncertainty markers detection in the biomedical field by the NLP community, as far as we know, no study has been conducted specifically on a morphosyntactic structure, such as the if-category.

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