Hedge Scope Detection in Biomedical Texts: An Effective Dependency-Based Method

Zhou, Huiwei; Deng, Hongda; Huang, Degen; Zhu, Min

doi:10.1371/journal.pone.0133715

Cited by 24 publications

(7 citation statements)

References 10 publications

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“…In these cases, the scope recognition fails because all tokens in a sentence can be taken as the scope. To deal with this problem, other studies have included the use of syntactic properties of the sentence to extract the scope ( Cotik et al, 2016 ; Zhou et al, 2015 ; Peng et al, 2018 ). Although rule-based approaches have been widely used in the biomedical domain, their main disadvantages are the large amount of time it takes to create rules manually, and the lack of flexibility and universality ( Zhou et al, 2018 ).…”

Section: Related Workmentioning

confidence: 99%

Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Pabón

Montenegro

Torrente

et al. 2022

PeerJ Computer Science

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Detecting negation and uncertainty is crucial for medical text mining applications; otherwise, extracted information can be incorrectly identified as real or factual events. Although several approaches have been proposed to detect negation and uncertainty in clinical texts, most efforts have focused on the English language. Most proposals developed for Spanish have focused mainly on negation detection and do not deal with uncertainty. In this paper, we propose a deep learning-based approach for both negation and uncertainty detection in clinical texts written in Spanish. The proposed approach explores two deep learning methods to achieve this goal: (i) Bidirectional Long-Short Term Memory with a Conditional Random Field layer (BiLSTM-CRF) and (ii) Bidirectional Encoder Representation for Transformers (BERT). The approach was evaluated using NUBES and IULA, two public corpora for the Spanish language. The results obtained showed an F-score of 92% and 80% in the scope recognition task for negation and uncertainty, respectively. We also present the results of a validation process conducted using a real-life annotated dataset from clinical notes belonging to cancer patients. The proposed approach shows the feasibility of deep learning-based methods to detect negation and uncertainty in Spanish clinical texts. Experiments also highlighted that this approach improves performance in the scope recognition task compared to other proposals in the biomedical domain.

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Section: Related Workmentioning

confidence: 99%

Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Pabón

Montenegro

Torrente

et al. 2022

PeerJ Computer Science

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show abstract

“…Recently as well, also researchers in the Natural Language Processing community have focused their attention on the detection of certainty and uncertainty markers and their linguistic scope (e.g. [10–18]). However, these studies tend.…”

Section: Introductionmentioning

confidence: 99%

Writers’ uncertainty in scientific and popular biomedical articles. A comparative analysis of the British Medical Journal and Discover Magazine

et al. 2019

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Distinguishing certain and uncertain information is of crucial importance both in the scientific field in the strict sense and in the popular scientific domain. In this paper, by adopting an epistemic stance perspective on certainty and uncertainty, and a mixed procedure of analysis, which combines a bottom-up and a top-down approach, we perform a comparative study (both qualitative and quantitative) of the uncertainty linguistic markers (verbs, non-verbs, modal verbs, conditional clauses, uncertain questions, epistemic future) and their scope in three different corpora: a historical corpus of 80 biomedical articles from the British Medical Journal (BMJ) 1840–2007; a corpus of 12 biomedical articles from BMJ 2013, and a contemporary corpus of 12 scientific popular articles from Discover 2013. The variables under observation are time, structure (IMRaD vs no-IMRaD) and genre (scientific vs popular articles). We apply the Generalized Linear Models analysis in order to test whether there are statistically significant differences (1) in the amount of uncertainty among the different corpora, and (2) in the categories of uncertainty markers used by writers. The results of our analysis reveal that (1) in all corpora, the percentages of uncertainty are always much lower than that of certainty; (2) uncertainty progressively diminishes over time in biomedical articles (in conjunction with their structural changes–IMRaD–and to the increase of the BMJ Impact Factor); and (3) uncertainty is slightly higher in scientific popular articles (Discover 2013) as compared to the contemporary corpus of scientific articles (BMJ 2013). Nevertheless, in all corpora, modal verbs are the most used uncertainty markers. These results suggest that not only do scientific writers prefer to communicate their uncertainty with markers of possibility rather than those of subjectivity but also that science journalists prefer using a third-person subject followed by modal verbs rather than a first-person subject followed by mental verbs such as think or believe .

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“…Distinguishing certain (= factual) and uncertain (= speculative) information in texts is of crucial importance in information extraction (IE) as well. Indeed, the detection of certainty/uncertainty markers and their linguistic 'scope' (Quirk et al 1985) has been receiving increasing attention in the Natural Language Processing (NLP) community (among the most known studies, Vincze et al 2008;Kim et al 2009;Ö zgür and Radev 2009;Agarwal and Yu 2010;Farkas et al 2010;Szarvas et al 2012;Zou et al 2013;Zhou et al 2011Zhou et al , 2015.…”

Section: Related Work On Uncertainty In Natural Language Processingmentioning

confidence: 99%

Writer’s uncertainty identification in scientific biomedical articles: a tool for automatic if-clause tagging

Omero

Valotto

Bellana

et al. 2020

Lang Resources & Evaluation

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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.

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Hedge Scope Detection in Biomedical Texts: An Effective Dependency-Based Method

Cited by 24 publications

References 10 publications

Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Writers’ uncertainty in scientific and popular biomedical articles. A comparative analysis of the British Medical Journal and Discover Magazine

Writer’s uncertainty identification in scientific biomedical articles: a tool for automatic if-clause tagging

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