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ZENODO
Software . 2019
License: CC BY NC SA
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Software . 2019
License: CC BY NC SA
Data sources: Datacite
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SPACCC_Tokenizer

Authors: Intxaurrondo, Ander; Krallinger, Martin;
Abstract

[PlanTL/medicine/document/NLP preprocessing/tokenization] The tokenization model trained using the SPACCC_TOKEN corpus (https://github.com/PlanTL-SANIDAD/SPACCC_TOKEN). The model was trained using the 90% of the corpus (900 clinical cases) and tested against the 10% (100 clinical cases). This model is a great resource to tokenize biomedical documents, specially clinical cases written in Spanish. This model was created using the Apache OpenNLP machine learning toolkit (https://opennlp.apache.org/), with the release number 1.8.4, released in December 2017. This repository contains the model, training set, testing set, Gold Standard, executable file, and the source code. Copyright (c) 2019 Secretaría de Estado para el Avance Digital

{"references": ["Villegas M, de la Pe\u00f1a S, Intxaurrondo A, Santamaria J, Krallinger M. Esfuerzos para fomentar la miner\u00eda de textos en biomedicina m\u00e1s all\u00e1 del ingl\u00e9s: el plan estrat\u00e9gico nacional espa\u00f1ol para las tecnolog\u00edas del lenguaje. Procesamiento del Lenguaje Natural. 2017(59):141-4."]}

Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).

Keywords

http://id.loc.gov/authorities/subjects/sh88002425, PlanTL, NLP preprocessing, Tokenization, Spanish, Corpus, Medical, Clinical Cases,, http://id.loc.gov/authorities/subjects/sh85083064

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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