The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text
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The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. / Pafilis, Evangelos; Pletscher-Frankild, Sune; Fanini, Lucia; Faulwetter, Sarah; Pavloudi, Christina; Vasileiadou, Aikaterini; Arvanitidis, Christos; Jensen, Lars Juhl.
In: PloS one, Vol. 8, No. 6, 18.06.2013, p. e65390.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text
AU - Pafilis, Evangelos
AU - Pletscher-Frankild, Sune
AU - Fanini, Lucia
AU - Faulwetter, Sarah
AU - Pavloudi, Christina
AU - Vasileiadou, Aikaterini
AU - Arvanitidis, Christos
AU - Jensen, Lars Juhl
PY - 2013/6/18
Y1 - 2013/6/18
N2 - The exponential growth of the biomedical literature is making the need for efficient, accurate text-mining tools increasingly clear. The identification of named biological entities in text is a central and difficult task. We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition, which we here use to identify names of species and other taxa in text. The tool, SPECIES, is more than an order of magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists. The SPECIES software is open source and can be downloaded from http://species.jensenlab.org along with dictionary files and the manually annotated gold-standard corpus. The ORGANISMS web resource can be found at http://organisms.jensenlab.org.
AB - The exponential growth of the biomedical literature is making the need for efficient, accurate text-mining tools increasingly clear. The identification of named biological entities in text is a central and difficult task. We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition, which we here use to identify names of species and other taxa in text. The tool, SPECIES, is more than an order of magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists. The SPECIES software is open source and can be downloaded from http://species.jensenlab.org along with dictionary files and the manually annotated gold-standard corpus. The ORGANISMS web resource can be found at http://organisms.jensenlab.org.
U2 - 10.1371/journal.pone.0065390
DO - 10.1371/journal.pone.0065390
M3 - Journal article
C2 - 23823062
VL - 8
SP - e65390
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 6
ER -
ID: 47418324