About: SciNER: Extracting Named Entities from Scientific Literature

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	SciNER: Extracting Named Entities from Scientific Literature
Creator	Hong, Zhi Chard, Kyle Foster, Ian Tchoua, Roselyne
source	PMC
abstract	The automated extraction of claims from scientific papers via computer is difficult due to the ambiguity and variability inherent in natural language. Even apparently simple tasks, such as isolating reported values for physical quantities (e.g., “the melting point of X is Y”) can be complicated by such factors as domain-specific conventions about how named entities (the X in the example) are referenced. Although there are domain-specific toolkits that can handle such complications in certain areas, a generalizable, adaptable model for scientific texts is still lacking. As a first step towards automating this process, we present a generalizable neural network model, SciNER, for recognizing scientific entities in free text. Based on bidirectional LSTM networks, our model combines word embeddings, subword embeddings, and external knowledge (from DBpedia) to boost its accuracy. Experiments show that our model outperforms a leading domain-specific extraction toolkit by up to 50%, as measured by F1 score, while also being easily adapted to new domains.
has issue date	2020-06-15(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50417-5_23
has license	no-cc
sha1sum (hex)	722c934697f4e98a99f60546afdb9b681c698ab9
schema:url	https://doi.org/10.1007/978-3-030-50417-5_23
resource representing a document's title	SciNER: Extracting Named Entities from Scientific Literature
has PubMed Central identifier	PMC7302801
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:722c934697f4e98a99f60546afdb9b681c698ab9#body_text
is schema:about of	named entity 'LSTM' named entity 'Literature' named entity 'ABOUT' named entity 'AREAS' named entity 'VARIABILITY' »more»

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