# Datasets: DFKI-SLT /brat

Language Creators: found
Annotations Creators: expert-generated
Dataset Preview
The dataset preview is not available for this dataset.
Cannot get the split names for the dataset.
Error code:   SplitsNamesError
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
File "/src/workers/splits/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 376, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/tmp/modules-cache/datasets_modules/datasets/DFKI-SLT--brat/95f9bada4009b8f29bd9a58fc18ebbc5e48e1397e590d2fef420c24685b10bf8/brat.py", line 308, in _split_generators
assert self.config.url is not None, "data url not specified"
AssertionError: data url not specified

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/src/workers/splits/src/splits/response.py", line 80, in get_splits_response
split_full_names = get_dataset_split_full_names(dataset=dataset, use_auth_token=use_auth_token)
File "/src/workers/splits/src/splits/response.py", line 37, in get_dataset_split_full_names
return [
File "/src/workers/splits/src/splits/response.py", line 40, in <listcomp>
for split in get_dataset_split_names(path=dataset, config_name=config, use_auth_token=use_auth_token)
File "/src/workers/splits/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 426, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/workers/splits/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 381, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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# Information Card for Brat

## Description

### Summary

Brat is an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annota- tion for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. brat is designed in particular for structured annotation, where the notes are not free form text but have a fixed form that can be automatically processed and interpreted by a computer.

## Dataset Structure

Dataset annotated with brat format is processed using this script. Annotations created in brat are stored on disk in a standoff format: annotations are stored separately from the annotated document text, which is never modified by the tool. For each text document in the system, there is a corresponding annotation file. The two are associatied by the file naming convention that their base name (file name without suffix) is the same: for example, the file DOC-1000.ann contains annotations for the file DOC-1000.txt. More information can be found here.

### Data Fields

-context: html content of data file as string
-file_name: a string name of file
-spans: a sequence containing id, type, location and text of a span
-relations: a sequence containing id, type and arguments of a relation
-equivalence_relations:
-events:
-normalizations:
-notes:


### Usage

brat script can be used by calling load_dataset() method and passing kwargs (arguments to the BuilderConfig) which should include at least url of the dataset prepared using brat. We provide an example of SciArg dataset below,

from datasets import load_dataset
kwargs = {
"description" :
"""This dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing
fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific
publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of
scientific writing.""",
"citation" :
"""@inproceedings{lauscher2018b,
title = {An argument-annotated corpus of scientific publications},
booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
publisher = {Association for Computational Linguistics},
author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
year = {2018},
pages = {40–46}
}""",
"homepage": "https://github.com/anlausch/ArguminSci",
"url": "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip",
"file_name_blacklist": ['A28'],
}



### Citation Information

@inproceedings{stenetorp-etal-2012-brat,
title = "brat: a Web-based Tool for {NLP}-Assisted Text Annotation",
author = "Stenetorp, Pontus  and
Pyysalo, Sampo  and
Topi{\'c}, Goran  and
Ohta, Tomoko  and