Datasets:
Tasks:
Token Classification
Sub-tasks:
parsing
Language Creators:
found
Annotations Creators:
expert-generated
File size: 13,202 Bytes
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import glob
import logging
from dataclasses import dataclass
from os import listdir, path
from typing import Dict, List, Optional, Union
import datasets
from datasets import BuilderConfig, DatasetInfo, Features, Sequence, SplitGenerator, Value
logger = logging.getLogger(__name__)
@dataclass
class BratConfig(BuilderConfig):
"""BuilderConfig for BRAT."""
url: str = None # type: ignore
description: Optional[str] = None
citation: Optional[str] = None
homepage: Optional[str] = None
# paths to directories or files per split (relative to url or data_dir)
split_paths: Optional[Dict[str, Union[str, List[str]]]] = None
file_name_blacklist: Optional[List[str]] = None
ann_file_extension: str = "ann"
txt_file_extension: str = "txt"
class Brat(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = BratConfig
def _info(self):
return DatasetInfo(
description=self.config.description,
citation=self.config.citation,
homepage=self.config.homepage,
features=Features(
{
"context": Value("string"),
"file_name": Value("string"),
"spans": Sequence(
{
"id": Value("string"),
"type": Value("string"),
"locations": Sequence(
{
"start": Value("int32"),
"end": Value("int32"),
}
),
"text": Value("string"),
}
),
"relations": Sequence(
{
"id": Value("string"),
"type": Value("string"),
"arguments": Sequence(
{"type": Value("string"), "target": Value("string")}
),
}
),
"equivalence_relations": Sequence(
{
"type": Value("string"),
"targets": Sequence(Value("string")),
}
),
"events": Sequence(
{
"id": Value("string"),
"type": Value("string"),
"trigger": Value("string"),
"arguments": Sequence(
{"type": Value("string"), "target": Value("string")}
),
}
),
"attributions": Sequence(
{
"id": Value("string"),
"type": Value("string"),
"target": Value("string"),
"value": Value("string"),
}
),
"normalizations": Sequence(
{
"id": Value("string"),
"type": Value("string"),
"target": Value("string"),
"resource_id": Value("string"),
"entity_id": Value("string"),
}
),
"notes": Sequence(
{
"id": Value("string"),
"type": Value("string"),
"target": Value("string"),
"note": Value("string"),
}
),
}
),
)
@staticmethod
def _get_location(location_string):
parts = location_string.split(" ")
assert (
len(parts) == 2
), f"Wrong number of entries in location string. Expected 2, but found: {parts}"
return {"start": int(parts[0]), "end": int(parts[1])}
@staticmethod
def _get_span_annotation(annotation_line):
"""
example input:
T1 Organization 0 4 Sony
"""
_id, remaining, text = annotation_line.split("\t", maxsplit=2)
_type, locations = remaining.split(" ", maxsplit=1)
return {
"id": _id,
"text": text,
"type": _type,
"locations": [Brat._get_location(loc) for loc in locations.split(";")],
}
@staticmethod
def _get_event_annotation(annotation_line):
"""
example input:
E1 MERGE-ORG:T2 Org1:T1 Org2:T3
"""
_id, remaining = annotation_line.strip().split("\t")
args = [dict(zip(["type", "target"], a.split(":"))) for a in remaining.split(" ")]
return {
"id": _id,
"type": args[0]["type"],
"trigger": args[0]["target"],
"arguments": args[1:],
}
@staticmethod
def _get_relation_annotation(annotation_line):
"""
example input:
R1 Origin Arg1:T3 Arg2:T4
"""
_id, remaining = annotation_line.strip().split("\t")
_type, remaining = remaining.split(" ", maxsplit=1)
args = [dict(zip(["type", "target"], a.split(":"))) for a in remaining.split(" ")]
return {"id": _id, "type": _type, "arguments": args}
@staticmethod
def _get_equivalence_relation_annotation(annotation_line):
"""
example input:
* Equiv T1 T2 T3
"""
_, remaining = annotation_line.strip().split("\t")
parts = remaining.split(" ")
return {"type": parts[0], "targets": parts[1:]}
@staticmethod
def _get_attribute_annotation(annotation_line):
"""
example input (binary: implicit value is True, if present, False otherwise):
A1 Negation E1
example input (multi-value: explicit value)
A2 Confidence E2 L1
"""
_id, remaining = annotation_line.strip().split("\t")
parts = remaining.split(" ")
# if no value is present, it is implicitly "true"
if len(parts) == 2:
parts.append("true")
return {
"id": _id,
"type": parts[0],
"target": parts[1],
"value": parts[2],
}
@staticmethod
def _get_normalization_annotation(annotation_line):
"""
example input:
N1 Reference T1 Wikipedia:534366 Barack Obama
"""
_id, remaining, text = annotation_line.split("\t", maxsplit=2)
_type, target, ref = remaining.split(" ")
res_id, ent_id = ref.split(":")
return {
"id": _id,
"type": _type,
"target": target,
"resource_id": res_id,
"entity_id": ent_id,
}
@staticmethod
def _get_note_annotation(annotation_line):
"""
example input:
#1 AnnotatorNotes T1 this annotation is suspect
"""
_id, remaining, note = annotation_line.split("\t", maxsplit=2)
_type, target = remaining.split(" ")
return {
"id": _id,
"type": _type,
"target": target,
"note": note,
}
@staticmethod
def _read_annotation_file(filename):
"""
reads a BRAT v1.3 annotations file (see https://brat.nlplab.org/standoff.html)
"""
res = {
"spans": [],
"events": [],
"relations": [],
"equivalence_relations": [],
"attributions": [],
"normalizations": [],
"notes": [],
}
with open(filename, encoding="utf-8") as file:
for i, line in enumerate(file):
if len(line.strip()) == 0:
continue
ann_type = line[0]
# strip away the new line character
if line.endswith("\n"):
line = line[:-1]
if ann_type == "T":
res["spans"].append(Brat._get_span_annotation(line))
elif ann_type == "E":
res["events"].append(Brat._get_event_annotation(line))
elif ann_type == "R":
res["relations"].append(Brat._get_relation_annotation(line))
elif ann_type == "*":
res["equivalence_relations"].append(
Brat._get_equivalence_relation_annotation(line)
)
elif ann_type in ["A", "M"]:
res["attributions"].append(Brat._get_attribute_annotation(line))
elif ann_type == "N":
res["normalizations"].append(Brat._get_normalization_annotation(line))
elif ann_type == "#":
res["notes"].append(Brat._get_note_annotation(line))
else:
raise ValueError(
f'unknown BRAT annotation id type: "{line}" (from file {filename} @line {i}). '
f"Annotation ids have to start with T (spans), E (events), R (relations), "
f"A (attributions), or N (normalizations). See "
f"https://brat.nlplab.org/standoff.html for the BRAT annotation file "
f"specification."
)
return res
def _generate_examples(self, base_dir: str, files: Optional[List[str]] = None, directory: Optional[str] = None):
"""Read context (.txt) and annotation (.ann) files."""
if files is None:
if directory is None:
raise ValueError("Either files or directory has to be provided.")
_directory = path.join(base_dir, directory)
_files = glob.glob(f"{_directory}/*.{self.config.ann_file_extension}")
files = sorted(path.splitext(fn)[0] for fn in _files)
if len(files) == 0:
raise ValueError(f"No files found in directory: {_directory}")
else:
if directory is not None:
raise ValueError("Only one of files or directory can be provided.")
files = [path.join(base_dir, fn) for fn in files]
for filename in files:
basename = path.basename(filename)
if (
self.config.file_name_blacklist is not None
and basename in self.config.file_name_blacklist
):
logger.info(f"skip annotation file: {basename} (blacklisted)")
continue
ann_fn = f"{filename}.{self.config.ann_file_extension}"
brat_annotations = Brat._read_annotation_file(ann_fn)
txt_fn = f"{filename}.{self.config.txt_file_extension}"
txt_content = open(txt_fn, encoding="utf-8").read()
brat_annotations["context"] = txt_content
brat_annotations["file_name"] = basename
yield basename, brat_annotations
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
if self.config.data_dir is not None:
data_dir = self.config.data_dir
logging.warning(f"load from data_dir: {data_dir}")
else:
# since subclasses of BuilderConfig are not allowed to define
# attributes without defaults, check here
assert self.config.url is not None, "data url not specified"
data_dir = dl_manager.download_and_extract(self.config.url)
# if no subdirectory mapping is provided, ...
if self.config.split_paths is None:
# ... use available subdirectories as split names ...
subdirs = [f for f in listdir(data_dir) if path.isdir(path.join(data_dir, f))]
if len(subdirs) > 0:
split_paths = {subdir: {"directory": subdir} for subdir in subdirs}
else:
# ... otherwise, default to a single train split with the base directory
split_paths = {"train": {"directory": ""}}
else:
split_paths = {}
for split, paths in self.config.split_paths.items():
if isinstance(paths, str):
split_paths[split] = {"directory": paths}
elif isinstance(paths, list):
split_paths[split] = {"files": paths}
else:
raise ValueError(
f"split_paths must be a dict containing either a single path to a directory "
f"or a list of file paths, but found: {paths}"
)
return [
SplitGenerator(
name=split,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"base_dir": data_dir,
**split_kwargs,
},
)
for split, split_kwargs in split_paths.items()
]
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