The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes

Vincze, Veronika; Szarvas, György; Farkas, Richárd; Móra, György; Csirik, János

doi:10.1186/1471-2105-9-s11-s9

Cited by 234 publications

(305 citation statements)

References 9 publications

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“…Ce problème de l'identification automatique d'indices modaux et de leur portée sur la base d'une analyse syntaxique a été principalement et seulement étudié dans les textes biomédicaux en anglais (Vincze et al, 2008). (Culioli, 1973), un indice (ou marqueur) peut tout aussi bien être lexical, grammatical ou relever d'une construction syntaxique.…”

Section: Le Phénomène De Prise En Charge éNonciative Et Modaleunclassified

Prise en charge et phénomènes de portée : retour d’expériences dans un corpus de dépêches de presse

Damiani

Battistelli

2014

SHS Web of Conferences

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Section: Le Phénomène De Prise En Charge éNonciative Et Modaleunclassified

Prise en charge et phénomènes de portée : retour d’expériences dans un corpus de dépêches de presse

Damiani

Battistelli

2014

SHS Web of Conferences

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“…The BioScope corpus (Vincze et al, 2008) is annotated for hedges and negations in sentences from biomedical domain; in this work, we use only the negation annotations. A negation (or hedge) annotation comprises a cue and a corresponding scope.…”

Section: Bioscope Corpusmentioning

confidence: 99%

“…The scope detection task is to identify the part(s) of the sentence that come under the scope of a negation cue. Scope detection is crucial for interpreting negations, and to that end, the BioScope corpus (Vincze et al, 2008) was released, with annotations of both negation cues and their associated scopes.…”

Section: Introductionmentioning

confidence: 99%

“…The first such corpus was BioScope (Vincze et al, 2008), which annotates negation cues and associated scopes in 3 genres-medical abstracts, scientific papers and clinical records. The BioNLP Event Extraction (EE) shared task corpus (Kim et al, 2009) also marks negation in the event annotations on sentences from molecular biology literature.…”

Section: Introductionmentioning

confidence: 99%

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Learning Structures of Negations from Flat Annotations

Prabhakaran

Boguraev²

2015

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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We propose a novel method to learn negation expressions in a specialized (medical) domain. In our corpus, negations are annotated as 'flat' text spans. This allows for some infelicities in the mark-up of the ground truth, making it less than perfectly aligned with the underlying syntactic structure. Nonetheless, the negations thus captured are correct in intent, and thus potentially valuable. We succeed in training a model for detecting the negated predicates corresponding to the annotated negations, by re-mapping the corpus to anchor its 'flat' annotation spans into the predicate argument structure. Our key idea-re-mapping the negation instance spans to more uniform syntactic nodes-makes it possible to re-frame the learning task as a simpler one, and to leverage an imperfect resource in a way which enables us to learn a high performance model. We achieve high accuracy for negation detection overall, 87%. Our re-mapping scheme can be constructively applied to existing flatly annotated resources for other tasks where syntactic context is vital.

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“…Several manually annotated corpora have been created, which serve as training and test databases of state-of-the-art uncertainty detectors based on supervised machine learning techniques. Most of these corpora are constructed for English, however, their domains and genres are diverse: biological texts (Medlock and Briscoe, 2007;Kim et al, 2008;Settles et al, 2008;Shatkay et al, 2008;Vincze et al, 2008;Nawaz et al, 2010) The diversity of the resources also manifests in the fact that the annotation principles behind the corpora might slightly differ, which led Szarvas et al (2012) to compare the annotation schemes of three corpora (BioScope, FactBank and WikiWeasel) and they offered a unified classification of semantic uncertainty phenomena, on the basis of which these corpora were reannotated, using uniform guidelines. Some other uncertainty-related linguistic phenomena are described as discourse-level uncertainty in Vincze (2013).…”

Section: Introductionmentioning

confidence: 99%

Annotating Uncertainty in Hungarian Webtext

Vincze¹,

Simkó²,

Varga³

2014

Proceedings of LAW VIII - The 8th Linguistic Annotation Workshop

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Uncertainty detection has been a popular topic in natural language processing, which manifested in the creation of several corpora for English. Here we show how the annotation guidelines originally developed for English standard texts can be adapted to Hungarian webtext. We annotated a small corpus of Facebook posts for uncertainty phenomena and we illustrate the main characteristics of such texts, with special regard to uncertainty annotation. Our results may be exploited in adapting the guidelines to other languages or domains and later on, in the construction of automatic uncertainty detectors. BackgroundDetecting uncertainty in natural language texts has received a considerable amount of attention in the last decade (Farkas et al., 2010;Morante and Sporleder, 2012). Several manually annotated corpora have been created, which serve as training and test databases of state-of-the-art uncertainty detectors based on supervised machine learning techniques. Most of these corpora are constructed for English, however, their domains and genres are diverse: biological texts (Medlock and Briscoe, 2007;Kim et al., 2008;Settles et al., 2008;Shatkay et al., 2008;Vincze et al., 2008;Nawaz et al., 2010) The diversity of the resources also manifests in the fact that the annotation principles behind the corpora might slightly differ, which led Szarvas et al. (2012) to compare the annotation schemes of three corpora (BioScope, FactBank and WikiWeasel) and they offered a unified classification of semantic uncertainty phenomena, on the basis of which these corpora were reannotated, using uniform guidelines. Some other uncertainty-related linguistic phenomena are described as discourse-level uncertainty in Vincze (2013). As a first objective of our paper, we will carry out a pilot study and investigate how these unified guidelines can be adapted to texts written in a language that is typologically different from English, namely, Hungarian.As a second goal, we will also focus on annotating texts in a new domain: social media textsapart from Wei et al. (2013) -have not been extensively investigated from the uncertainty detection perspective. As the use and communication through the internet is becoming more and more important in people's lives, the huge amount of data available from this domain is a valuable source of information for computation linguistics. However, processing texts from the web -especially social media texts from blogs, status updates, chat logs and comments -revealed that they are very challenging for applications trained on standard texts. Most studies in this area focus on English, for instance, sentiment analysis from tweets has been the focus of recent challenges (Wilson et al., 2013) and Facebook posts have been analysed from the perspective of computational psychology (Celli et al., 2013). A syntactically

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The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes

Cited by 234 publications

References 9 publications

Prise en charge et phénomènes de portée : retour d’expériences dans un corpus de dépêches de presse

Prise en charge et phénomènes de portée : retour d’expériences dans un corpus de dépêches de presse

Learning Structures of Negations from Flat Annotations

Annotating Uncertainty in Hungarian Webtext

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