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61-80 / 485 すべて表示
jnlpba-st-training The training data used in the task came from the GENIA version 3.02 corpus, This was formed from a controlled search on MEDLINE using the MeSH terms "human", "blood cells" and "transcription factors". From this search, 1,999 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus. For the shared task only the classes protein, DNA, RNA, cell line and cell type were used. The first three incorporate several subclasses from the original taxonomy while the last two are interesting in order to make the task realistic for post-processing by a potential template filling application. The publication year of the training set ranges over 1990~1999.51.1 KGENIAYue Wang2021-03-10公開中
GENIAcorpus multi_cell (1,782) mono_cell (222) virus (2,136) protein_family_or_group (8,002) protein_complex (2,394) protein_molecule (21,290) protein_subunit (942) protein_substructure (129) protein_domain_or_region (1,044) protein_other (97) peptide (521) amino_acid_monomer (784) DNA_family_or_group (332) DNA_molecule (664) DNA_substructure (2) DNA_domain_or_region (39) DNA_other (16) RNA_family_or_group (1,545) RNA_molecule (554) RNA_substructure (106) RNA_domain_or_region (8,237) RNA_other (48) polynucleotide (259) nucleotide (243) lipid (2,375) carbohydrate (99) other_organic_compound (4,113) body_part (461) tissue (706) cell_type (7,473) cell_component (679) cell_line (4,129) other_artificial_source (211) inorganic (258) atom (342) other (21,056) 78.9 KGENIA ProjectYue Wang2021-03-10公開中
pubmed-sentences-benchmark A benchmark data for text segmentation into sentences. The source of annotation is the GENIA treebank v1.0. Following is the process taken. began with the GENIA treebank v1.0. sentence annotations were extracted and converted to PubAnnotation JSON. uploaded. 12 abstracts met alignment failure. among the 12 failure cases, 4 had a dot('.') character where there should be colon (':'). They were manually fixed then successfully uploaded: 7903907, 8053950, 8508358, 9415639. among the 12 failed abstracts, 8 were "250 word truncation" cases. They were manually fixed and successfully uploaded. During the fixing, manual annotations were added for the missing pieces of text. 30 abstracts had extra text in the end, indicating copyright statement, e.g., "Copyright 1998 Academic Press." They were annotated as a sentence in GTB. However, the text did not exist anymore in PubMed. Therefore, the extra texts were removed, together with the sentence annotation to them. 18.4 KGENIA projectJin-Dong Kim2021-03-10公開中
NCBIDiseaseCorpus The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community.6.85 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2021-03-10公開中
PubMed_ArguminSci Predictions for PubMed automatically extracted with the ArguminSci tool (https://github.com/anlausch/ArguminSci).777 Kzebet2021-03-10公開中
spacy-test Random set of articles used for testing in the development of the RESTful spaCy parsing web service. Since development is now finished, they are released for the community to use.131 KNico ColicNico Colic2021-03-10公開中
DisGeNET5_gene_disease The file contains gene-disease associations obtained by text mining MEDLINE abstracts using the BeFree system including the gene and disease off sets.2.04 MIBI GroupYue Wang2021-03-11公開中
2015-BEL-Sample-2 The 295 BEL statements for sample set used for the 2015 BioCreative challenge.11.4 KFabio RinaldiNico Colic2021-03-11公開中
LitCovid-OGER Using OGER (http://www.ontogene.org/resources/oger) to detect entities from 10 different vocabularies9.31 KFabio RinaldiNico Colic2021-05-27公開中
LitCovid-OGER-BB Using OGER (www.ontogene.com) and Biobert to obtain annotations for 10 different vocabularies.308 KFabio RinaldiNico Colic2021-05-27公開中
Genomics_Informatics Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining.35.3 KHyun-Seok Parkewha-bio2018-11-27ベータ
LitCovid-PAS-Enju Predicate-argument structure annotation produced by the Enju parser.125 KJin-Dong Kim2020-03-25ベータ
blah6_medical_device BLAH6 hackathon project to annotate medical device indications in premarket approval statement summaries. The documents in this project serve as a corpus of premarket approval (PMA) statements that have undergone quality control. In particular, we have (1) removed non-ascii characters, (2) fixed some text segmentation errors, and (3) fixed some capitalization errors.0Stefano Rensitherightstef2020-08-04ベータ
FA_Top107-forWeb ※※※ !要データ加工! webリンク用には、この結果を加工して使っています。その他で使われる場合に、末尾記載の問題を別途解決する必要があります。 !要データ加工! ※※※ Top100+本来Top100に入るべきだった7レビューの計、107レビュー中99レビュー。 5414, 6076, 6930, 8403, 9643, 12112, 18544, 18829は、0denotationでドキュメント自体登録していません。 @AikoHIRAKIはtypoを修正したレビューフォルダ。 attributesの詳細はconfig参照。 ※※※ !注意! webリンク側のしばりで、選択文字列は複数のUniProtIDに対応していません。(例)Protein1~Protein7とある場合、 Protein1, 2, 3, 4, 5, 6, 7をさし、かつ全てにUniProtIDがあったとしても、1と7のみUniProtIDをとってきています。 "~"は、Protein2, 3, 4, 5, 6を意味していますが、positionではなく文字列で検索をかけているのと、見せ方の仕様上、これらのIDは全て未取得となっています。⇔GeneProteinでは"~"に2-6のIDsをもたせていました。 該当レビュー;14898(~=MAPK2, MAPK3, MAPK4, MAPK5, MAPK6), 10471(~=Ago2, Ago3) --------------------------------------- (例)ProteinAB...ProteinCD...ProteinB...ProteinDとある場合、 ProteinABは、ProteinAとBというLexical_Cueになっています。ProteinCDも同様に、ProteinCとD。BとDだけでは、このレビュー内ではProteinBやProteinDをさすことが分かるのですが、それ以外で使用する場合に、BとDにそれぞれ該当UniProtIDをあてるのは不適切です。 該当レビュー;11957(β4=itgb4, β1=itgb1, β5=itgb5, β3=itgb3) 他の例が出てきたら順次、ここに記載していきます。当座、これらは削除する必要があります。 attributeで削除フラグをつけるか、Jakeの機能がTextAEに実装されれば解決するか、検討して、何かしら分かるようにしておきます。 (例)ProteinA/B とある場合、 webリンクでは、"ProteinA"にUniProtID-Aを、"/B"にUniProtID-Bをつけています(リンク側のしばり)。webリンク以外で使われる場合には、別プロジェクトのFA_Top100Plus-GeneProteinで行っていたようなRelationを使って、"/B"ではなく、"ProteinB"として、UniProtID-Bと対応させる必要があります。現状のとり方ですと、要Relation箇所は救済出来ません。 Lexical cueには"/B"とありますが、Objectには"ProteinB"と残してあるので、Objectを参照して下さい。 但し、言語処理のようなpositionがご入用な場合には上では対応出来ていません。 該当レビュー;11935(/4=BMP4), 14898(/2=LATS2), 7412(/2=TSC2), 4629(/2=CtBP2) (webリンクでは、レビュー毎に完結しているので、"/B"がそのレビューで他の意味をなしていなければ対応出来るのと、文字列合致でリンクを貼っているためです。) !注意! ※※※ RelationのmergedはTextAEの既存機能で既に出来ます。10.3 KAikoHIRAKI2020-09-01ベータ
bionlp-st-ge-2016-uniprot UniProt protein annotation to the benchmark data set of BioNLP-ST 2016 GE task: reference data set (bionlp-st-ge-2016-reference) and test data set (bionlp-st-ge-2016-test). The annotations are produced based on a dictionary which is semi-automatically compiled for the 34 full paper articles included in the benchmark data set (20 in the reference data set + 14 in the test data set). For detailed information about BioNLP-ST GE 2016 task data sets, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test). 16.2 KDBCLSJin-Dong Kim2020-10-02ベータ
Age_blah 1.9 Kslee72682020-11-03ベータ
LitCovid-sample-sentences 2.3 KJin-Dong Kim2021-01-14ベータ
LitCovid-sample-PD-FMA 1.93 KJin-Dong Kim2021-01-14ベータ
LitCovid-sample-PD-IDO 1.27 KJin-Dong Kim2021-01-14ベータ
LitCovid-sample-PD-UBERON PubDictionaries annotation for UBERON terms - updated at 2020-04-30 It is annotation for anatomical entities based on Uberon. The terms in Uberon are uploaded in PubDictionaries (Uberon), with which the annotations in this project are produced. The parameter configuration used for this project is here. Note that it is an automatically generated dictionary-based annotation. It will be updated periodically, as the documents are increased, and the dictionary is improved. 310Jin-Dong Kim2021-01-14ベータ
NameT# Ann.AuthorMaintainerUpdated_atStatus

61-80 / 485 すべて表示
jnlpba-st-training 51.1 KGENIAYue Wang2021-03-10公開中
GENIAcorpus 78.9 KGENIA ProjectYue Wang2021-03-10公開中
pubmed-sentences-benchmark 18.4 KGENIA projectJin-Dong Kim2021-03-10公開中
NCBIDiseaseCorpus 6.85 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2021-03-10公開中
PubMed_ArguminSci 777 Kzebet2021-03-10公開中
spacy-test 131 KNico ColicNico Colic2021-03-10公開中
DisGeNET5_gene_disease 2.04 MIBI GroupYue Wang2021-03-11公開中
2015-BEL-Sample-2 11.4 KFabio RinaldiNico Colic2021-03-11公開中
LitCovid-OGER 9.31 KFabio RinaldiNico Colic2021-05-27公開中
LitCovid-OGER-BB 308 KFabio RinaldiNico Colic2021-05-27公開中
Genomics_Informatics 35.3 KHyun-Seok Parkewha-bio2018-11-27ベータ
LitCovid-PAS-Enju 125 KJin-Dong Kim2020-03-25ベータ
blah6_medical_device 0Stefano Rensitherightstef2020-08-04ベータ
FA_Top107-forWeb 10.3 KAikoHIRAKI2020-09-01ベータ
bionlp-st-ge-2016-uniprot 16.2 KDBCLSJin-Dong Kim2020-10-02ベータ
Age_blah 1.9 Kslee72682020-11-03ベータ
LitCovid-sample-sentences 2.3 KJin-Dong Kim2021-01-14ベータ
LitCovid-sample-PD-FMA 1.93 KJin-Dong Kim2021-01-14ベータ
LitCovid-sample-PD-IDO 1.27 KJin-Dong Kim2021-01-14ベータ
LitCovid-sample-PD-UBERON 310Jin-Dong Kim2021-01-14ベータ