| bionlp-ost-19-BB-norm-dev | | | 1.33 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-norm-ner-dev | | | 1.33 K | | ldeleger | 2023-11-27 | Developing | |
| bionlp-ost-19-BB-norm-ner-test | | | 125 | | ldeleger | 2023-11-27 | Developing | |
| bionlp-ost-19-BB-norm-ner-train | | | 2.45 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-norm-test | | | 2.05 K | | ldeleger | 2023-11-28 | Developing | |
| bionlp-ost-19-BB-norm-train | | | 2.45 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-rel-dev | | | 1.97 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-rel-ner-dev | | | 1.98 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-rel-ner-test | | | 125 | | ldeleger | 2023-11-24 | Developing | |
| bionlp-ost-19-BB-rel-ner-train | | | 3.62 K | | ldeleger | 2023-11-28 | Developing | |
| bionlp-ost-19-BB-rel-test | | | 2.07 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-rel-train | | | 3.52 K | | ldeleger | 2023-11-28 | Developing | |
| bionlp-ost-19-SeeDev-bin-dev | | | 2.58 K | | ldeleger | 2023-11-28 | Developing | |
| bionlp-ost-19-SeeDev-bin-test | | | 2.32 K | | ldeleger | 2023-11-28 | Developing | |
| bionlp-ost-19-SeeDev-bin-train | | | 5.08 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-st-2016-SeeDev-dev | | Entities and event annotations from the development set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 61 | | EstelleChaix | 2023-11-29 | Released | |
| bionlp-st-2016-SeeDev-test | | Entities annotations from the test set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 184 | | EstelleChaix | 2023-11-29 | Released | |
| bionlp-st-2016-SeeDev-training | | Entities and event annotations from the training set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 35 | | EstelleChaix | 2023-11-28 | Released | |
| bionlp-st-bb3-2016-training | | Entity (bacteria, habitats and geographical places) annotation to the training dataset of the BioNLP-ST 2016 BB task.
For more information, please refer to bionlp-st-bb3-2016-development and bionlp-st-bb3-2016-test.
Bacteria
Bacteria entities are annotated as contiguous spans of text that contains a full unambiguous prokaryote taxon name, the type label is Bacteria. The Bacteria type is a taxon, at any taxonomic level from phylum (Eubacteria) to strain. The category that the text entities have to be assigned to is the most specific and unique category of the NCBI taxonomy resource. In case a given strain, or a group of strains is not referenced by NCBI, it is assigned with the closest taxid in the taxonomy.
Habitat
Habitat entities are annotated as spans of text that contains a complete mention of a potential habitat for bacteria, the type label is Habitat. Habitat entities are assigned one or several concepts from the habitat subpart of the OntoBiotope ontology. The assigned concepts are as specific as possible. OntoBiotope defines most relevant microorganism habitats from all areas considered by microbial ecology (hosts, natural environment, anthropized environments, food, medical, etc.). Habitat entities are rarely referential entities, they are usually noun phrases including properties and modifiers. There are rare cases of habitats referred with adjectives or verbs. The spans are generally contiguous but some of them are discontinuous in order to cope with conjunctions.
Geographical
Geographical entities are geographical and organization places denoted by official names. | 1.28 K | INRA | Yue Wang | 2023-11-29 | Released | |
| bionlp-st-cg-2013-training | | The training dataset from the cancer genetics task in the BioNLP Shared Task 2013.
Composed of anatomical and molecular entities. | 10.9 K | NaCTeM | Yue Wang | 2023-11-28 | Released | |
