Beyond genes, proteins, and abstracts: Identifying scientific claims from full-text biomedical articles

Blake, Catherine

doi:10.1016/j.jbi.2009.11.001

Cited by 70 publications

(54 citation statements)

References 21 publications

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“…In all of these studies, the annotators have to label argument components (typically, each sentence represents exactly one argument component) with one out of 3 -15 categories. In most of Blake (2010) 29 papers (Biomedicine) 2 (students) 5 discussion 0.57-0.88 Table 1: Comparison of annotation studies on scientific full-texts (CL = computational linguistics, #Cat = number of categories which can be annotated, IAA = chance-corrected inter-annotator agreement).…”

Section: Related Workmentioning

confidence: 99%

Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications

Kirschner¹,

Eckle‐Kohler²,

Gurevych³

2015

Proceedings of the 2nd Workshop on Argumentation Mining

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This paper presents the results of an annotation study focused on the fine-grained analysis of argumentation structures in scientific publications. Our new annotation scheme specifies four types of binary argumentative relations between sentences, resulting in the representation of arguments as small graph structures. We developed an annotation tool that supports the annotation of such graphs and carried out an annotation study with four annotators on 24 scientific articles from the domain of educational research. For calculating the inter-annotator agreement, we adapted existing measures and developed a novel graphbased agreement measure which reflects the semantic similarity of different annotation graphs.

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Section: Related Workmentioning

confidence: 99%

Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications

Kirschner¹,

Eckle‐Kohler²,

Gurevych³

2015

Proceedings of the 2nd Workshop on Argumentation Mining

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“…Blake (2010) introduced the Claim Framework to differentiate levels of evidence, such as comparisons and observations, in implicit and explicit claims in biomedical domain literature. The HypothesisFinder (Malhotra et al, 2013) uses machine learning techniques to classify sentences in scientific literature in order to find speculative sentences.…”

Section: Rhetorical Entitiesmentioning

confidence: 99%

Semantic representation of scientific literature: bringing claims, contributions and named entities onto the Linked Open Data cloud

Sateli

Witte

2015

PeerJ Computer Science

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Motivation. Finding relevant scientific literature is one of the essential tasks researchers are facing on a daily basis. Digital libraries and web information retrieval techniques provide rapid access to a vast amount of scientific literature. However, no further automated support is available that would enable fine-grained access to the knowledge 'stored' in these documents. The emerging domain of Semantic Publishing aims at making scientific knowledge accessible to both humans and machines, by adding semantic annotations to content, such as a publication's contributions, methods, or application domains. However, despite the promises of better knowledge access, the manual annotation of existing research literature is prohibitively expensive for wide-spread adoption. We argue that a novel combination of three distinct methods can significantly advance this vision in a fully-automated way: (i) Natural Language Processing (NLP) for Rhetorical Entity (RE) detection; (ii) Named Entity (NE) recognition based on the Linked Open Data (LOD) cloud; and (iii) automatic knowledge base construction for both NEs and REs using semantic web ontologies that interconnect entities in documents with the machine-readable LOD cloud.Results. We present a complete workflow to transform scientific literature into a semantic knowledge base, based on the W3C standards RDF and RDFS. A text mining pipeline, implemented based on the GATE framework, automatically extracts rhetorical entities of type Claims and Contributions from full-text scientific literature. These REs are further enriched with named entities, represented as URIs to the linked open data cloud, by integrating the DBpedia Spotlight tool into our workflow. Text mining results are stored in a knowledge base through a flexible export process that provides for a dynamic mapping of semantic annotations to LOD vocabularies through rules stored in the knowledge base. We created a gold standard corpus from computer science conference proceedings and journal articles, where Claim and Contribution sentences are manually annotated with their respective types using LOD URIs. The performance of the RE detection phase is evaluated against this corpus, where it achieves an average F-measure of 0.73. We further demonstrate a number of semantic queries that show how the generated knowledge base can provide support for numerous use cases in managing scientific literature. Availability. All software presented in this paper is available under open source licenses at http://www.semanticsoftware.info/semantic-scientific-literature-peerj-2015-supplements. Development releases of individual components are additionally available on our GitHub page at https://github.com/SemanticSoftwareLab.

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“…Moreover, not every single phrase is indexed in PubMed. All these publications might then be missed out by a systematic search of titles, abstracts and keywords on PubMed [12,13]. In contrast, a search of full-text articles would enhance the publication retrieval [14].…”

Section: Introductionmentioning

confidence: 99%

Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis

Blanc

Collet

Auer

et al. 2014

BMC Med Inform Decis Mak

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BackgroundShared Decision Making (SDM) is increasingly advocated as a model for medical decision making. However, there is still low use of SDM in clinical practice. High impact factor journals might represent an efficient way for its dissemination. We aimed to identify and characterize publication trends of SDM in 15 high impact medical journals.MethodsWe selected the 15 general and internal medicine journals with the highest impact factor publishing original articles, letters and editorials. We retrieved publications from 1996 to 2011 through the full-text search function on each journal website and abstracted bibliometric data. We included publications of any type containing the phrase “shared decision making” or five other variants in their abstract or full text. These were referred to as SDM publications. A polynomial Poisson regression model with logarithmic link function was used to assess the evolution across the period of the number of SDM publications according to publication characteristics.ResultsWe identified 1285 SDM publications out of 229,179 publications in 15 journals from 1996 to 2011. The absolute number of SDM publications by journal ranged from 2 to 273 over 16 years. SDM publications increased both in absolute and relative numbers per year, from 46 (0.32% relative to all publications from the 15 journals) in 1996 to 165 (1.17%) in 2011. This growth was exponential (P < 0.01). We found fewer research publications (465, 36.2% of all SDM publications) than non-research publications, which included non-systematic reviews, letters, and editorials. The increase of research publications across time was linear. Full-text search retrieved ten times more SDM publications than a similar PubMed search (1285 vs. 119 respectively).ConclusionThis review in full-text showed that SDM publications increased exponentially in major medical journals from 1996 to 2011. This growth might reflect an increased dissemination of the SDM concept to the medical community.

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Beyond genes, proteins, and abstracts: Identifying scientific claims from full-text biomedical articles

Cited by 70 publications

References 21 publications

Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications

Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications

Semantic representation of scientific literature: bringing claims, contributions and named entities onto the Linked Open Data cloud

Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis

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