About: Time Expressions Identification Without Human-Labeled Corpus for Clinical Text Mining in Russian

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Time Expressions Identification Without Human-Labeled Corpus for Clinical Text Mining in Russian
Creator	Funkner, Anastasia Kovalchuk, Sergey
Source	PMC
abstract	To obtain accurate predictive models in medicine, it is necessary to use complete relevant information about the patient. We propose an approach for extracting temporary expressions from unlabeled natural language texts. This approach can be used for the first analysis of the corpus, for data labeling as the first stage, or for obtaining linguistic constructions that can be used for a rule-based approach to retrieve information. Our method includes the sequential use of several machine learning and natural language processing methods: classification of sentences, the transformation of word bag frequencies, clustering of sentences with time expressions, classification of new data into clusters and construction of sentence profiles using feature importances. With this method, we derive the list of the most frequent time expressions and extract events and/or time events for 9801 sentences of anamnesis in Russian. The proposed approach is independent of the corpus language and can be used for other tasks, for example, extracting an experiencer of a disease.
has issue date	2020-05-23(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50423-6_44
has license	no-cc
sha1sum (hex)	4e10f1cd70b4f72101045134f5094fa31627de46
schema:url	https://doi.org/10.1007/978-3-030-50423-6_44
resource representing a document's title	Time Expressions Identification Without Human-Labeled Corpus for Clinical Text Mining in Russian
has PubMed Central identifier	PMC7303688
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:4e10f1cd70b4f72101045134f5094fa31627de46#body_text
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