

# Dataset Card for BioASQ Task B

## Dataset Description

- **Homepage:** http://participants-area.bioasq.org/datasets/
- **Pubmed:** True
- **Public:** False
- **Tasks:** QA


The BioASQ corpus contains multiple question
answering tasks annotated by biomedical experts, including yes/no, factoid, list,
and summary questions. Pertaining to our objective of comparing neural language
models, we focus on the the yes/no questions (Task 7b), and leave the inclusion
of other tasks to future work. Each question is paired with a reference text
containing multiple sentences from a PubMed abstract and a yes/no answer. We use
the official train/dev/test split of 670/75/140 questions.

See 'Domain-Specific Language Model Pretraining for Biomedical
Natural Language Processing'


## Citation Information

```
@article{tsatsaronis2015overview,
	title        = {
		An overview of the BIOASQ large-scale biomedical semantic indexing and
		question answering competition
	},
	author       = {
		Tsatsaronis, George and Balikas, Georgios and Malakasiotis, Prodromos
        and Partalas, Ioannis and Zschunke, Matthias and Alvers, Michael R and
		Weissenborn, Dirk and Krithara, Anastasia and Petridis, Sergios and
		Polychronopoulos, Dimitris and others
	},
	year         = 2015,
	journal      = {BMC bioinformatics},
	publisher    = {BioMed Central Ltd},
	volume       = 16,
	number       = 1,
	pages        = 138
}
```

multilinguality:monolingual language:en license:other region:us
{"language": ["en"], "license": "other", "multilinguality": "monolingual", "pretty_name": "BioASQ Task B", "bigbio_language": ["English"], "bigbio_license_shortname": "NLM_LICENSE", "homepage": "http://participants-area.bioasq.org/datasets/", "bigbio_pubmed": true, "bigbio_public": false, "bigbio_tasks": ["QUESTION_ANSWERING"]}
TAGS
#multilinguality-monolingual #language-English #license-other #region-us 



# Dataset Card for BioASQ Task B

## Dataset Description

- Homepage: URL
- Pubmed: True
- Public: False
- Tasks: QA


The BioASQ corpus contains multiple question
answering tasks annotated by biomedical experts, including yes/no, factoid, list,
and summary questions. Pertaining to our objective of comparing neural language
models, we focus on the the yes/no questions (Task 7b), and leave the inclusion
of other tasks to future work. Each question is paired with a reference text
containing multiple sentences from a PubMed abstract and a yes/no answer. We use
the official train/dev/test split of 670/75/140 questions.

See 'Domain-Specific Language Model Pretraining for Biomedical
Natural Language Processing'






# Dataset Card for BioASQ Task B ## Dataset Description

- Homepage: URL
- Pubmed: True
- Public: False
- Tasks: QA


The BioASQ corpus contains multiple question
answering tasks annotated by biomedical experts, including yes/no, factoid, list,
and summary questions. Pertaining to our objective of comparing neural language
models, we focus on the the yes/no questions (Task 7b), and leave the inclusion
of other tasks to future work. Each question is paired with a reference text
containing multiple sentences from a PubMed abstract and a yes/no answer. We use
the official train/dev/test split of 670/75/140 questions.

See 'Domain-Specific Language Model Pretraining for Biomedical
Natural Language Processing'
TAGS
#multilinguality-monolingual #language-English #license-other #region-us 
 # Dataset Card for BioASQ Task B ## Dataset Description

- Homepage: URL
- Pubmed: True
- Public: False
- Tasks: QA


The BioASQ corpus contains multiple question
answering tasks annotated by biomedical experts, including yes/no, factoid, list,
and summary questions. Pertaining to our objective of comparing neural language
models, we focus on the the yes/no questions (Task 7b), and leave the inclusion
of other tasks to future work. Each question is paired with a reference text
containing multiple sentences from a PubMed abstract and a yes/no answer. We use
the official train/dev/test split of 670/75/140 questions.

See 'Domain-Specific Language Model Pretraining for Biomedical
Natural Language Processing'