GENIA Tagger statistics

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Citations per year

Number of citations per year for the bioinformatics software tool GENIA Tagger
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Tool usage distribution map

This map represents all the scientific publications referring to GENIA Tagger per scientific context
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Associated diseases

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Protocols

GENIA Tagger specifications

Information


Unique identifier OMICS_05279
Name GENIA Tagger
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Version 3.0
Stability Stable
Maintained Yes

Versioning


No version available

GENIA Tagger citations

 (57)
library_books

Classification and analysis of a large collection of in vivo bioassay descriptions

2017
PLoS Comput Biol
PMCID: 5517062
PMID: 28678787
DOI: 10.1371/journal.pcbi.1005641

[…] Following preprocessing, we used the GENIA tagger (version 3.0.2, February 2016) [, ] to tokenize and annotate the descriptions with shallow linguistic features such as POS and chunk tags. GENIA is tuned to the analysis of English biomed […]

library_books

Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

2016
PMCID: 5088735
PMID: 27777244
DOI: 10.1093/database/baw140

[…] tta’, from which features are extracted for training the CRF-based DNER model.The feature set is listed below: Word: The word itself.POS: The part-of-speech tag of each word which is generated by the GENIA tagger.Chunk: The chunking information for each word which is generated by the GENIA tagger.Word Shape: Here, we represent each word by its shape information. If a character is in upper case, we […]

library_books

BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID

2016
PMCID: 5009341
PMID: 27589962
DOI: 10.1093/database/baw121

[…] loped tools for data pre-processing: the LingPipe sentence splitter for detecting sentence boundaries (http://alias-i.com/lingpipe), OSCAR4’s tokenizer for segmenting sentences into tokens () and the GENIA Tagger for lemmatization as well as part-of-speech and chunk tagging (). The recognition of gene/protein (F-score 70%) and organism mentions (F-score 73%) in text was addressed by training Condi […]

library_books

Argo: enabling the development of bespoke workflows and services for disease annotation

2016
PMCID: 4869796
PMID: 27189607
DOI: 10.1093/database/baw066

[…] tter (http://tinyurl.com/hv9m9tg). These in turn were decomposed into tokens by the OSCAR4 Tokeniser () which were then assigned lemmatised forms as well as part-of-speech (POS) and chunk tags by the GENIA Tagger (). Figure 4. We employed the NERsuite package (http://nersuite.nlplab.org), an implementation of conditional random fields (CRFs) (), to apply pre-trained models for sequence labelling. […]

library_books

Building a glaucoma interaction network using a text mining approach

2016
BioData Min
PMCID: 4857381
PMID: 27152122
DOI: 10.1186/s13040-016-0096-2

[…] #ofrelevantretrievedinstances#ofrelevantinstances,F1=2*P*RP+R(1)The text retrieval step performance metrics and values are listed in Table  and Table . For the entity extraction step performance, the GENIA tagger targets a broader domain. Hence, it can be expected to tag varied entities (including localization, cell type, DNA, etc.), but possibly less genes/proteins than the GenTag tagger. This is […]

library_books

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases

2016
J Biomed Semantics
PMCID: 4877743
PMID: 27216254
DOI: 10.1186/s13326-015-0044-y

[…] . Chunking is the task of identifying and grouping words in a sentence into constituents (noun groups, verb groups etc.) called “chunks”. Sentences are tagged with part-of-speech (POS) tags using the Genia Tagger []. We further chunk the words based on syntactically related POS tags to form noun phrases (NPs), verb groups (VGs) and prepositional phrases (PPs).After chunking, we use iSimp [], which […]


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