Datasets:
File size: 12,726 Bytes
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import glob
from dataclasses import dataclass
from typing import Dict, List
from pathlib import Path
import datasets
def remove_prefix(a: str, prefix: str) -> str:
if a.startswith(prefix):
a = a[len(prefix) :]
return a
def parse_brat_file(
txt_file: Path,
annotation_file_suffixes: List[str] = None,
parse_notes: bool = False,
) -> Dict:
"""
Parse a brat file into the schema defined below.
`txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
Will include annotator notes, when `parse_notes == True`.
brat_features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text": datasets.Value("string"),
"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
{
"offsets": datasets.Sequence([datasets.Value("int32")]),
"text": datasets.Sequence(datasets.Value("string")),
"type": datasets.Value("string"),
"id": datasets.Value("string"),
}
],
"events": [ # E line in brat
{
"trigger": datasets.Value(
"string"
), # refers to the text_bound_annotation of the trigger,
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"arguments": datasets.Sequence(
{
"role": datasets.Value("string"),
"ref_id": datasets.Value("string"),
}
),
}
],
"relations": [ # R line in brat
{
"id": datasets.Value("string"),
"head": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"tail": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"type": datasets.Value("string"),
}
],
"equivalences": [ # Equiv line in brat
{
"id": datasets.Value("string"),
"ref_ids": datasets.Sequence(datasets.Value("string")),
}
],
"attributes": [ # M or A lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"value": datasets.Value("string"),
}
],
"normalizations": [ # N lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"resource_name": datasets.Value(
"string"
), # Name of the resource, e.g. "Wikipedia"
"cuid": datasets.Value(
"string"
), # ID in the resource, e.g. 534366
"text": datasets.Value(
"string"
), # Human readable description/name of the entity, e.g. "Barack Obama"
}
],
### OPTIONAL: Only included when `parse_notes == True`
"notes": [ # # lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"text": datasets.Value("string"),
}
],
},
)
"""
example = {}
example["document_id"] = txt_file.with_suffix("").name
with txt_file.open() as f:
example["text"] = f.read()
# If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
# for event extraction
if annotation_file_suffixes is None:
annotation_file_suffixes = [".a1", ".a2", ".ann"]
if len(annotation_file_suffixes) == 0:
raise AssertionError(
"At least one suffix for the to-be-read annotation files should be given!"
)
ann_lines = []
for suffix in annotation_file_suffixes:
annotation_file = txt_file.with_suffix(suffix)
if annotation_file.exists():
with annotation_file.open() as f:
ann_lines.extend(f.readlines())
example["text_bound_annotations"] = []
example["events"] = []
example["relations"] = []
example["equivalences"] = []
example["attributes"] = []
example["normalizations"] = []
if parse_notes:
example["notes"] = []
for line in ann_lines:
line = line.strip()
if not line:
continue
if line.startswith("T"): # Text bound
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["type"] = fields[1].split()[0]
ann["offsets"] = []
span_str = remove_prefix(fields[1], (ann["type"] + " "))
text = fields[2]
for span in span_str.split(";"):
start, end = span.split()
ann["offsets"].append([int(start), int(end)])
# Heuristically split text of discontiguous entities into chunks
ann["text"] = []
if len(ann["offsets"]) > 1:
i = 0
for start, end in ann["offsets"]:
chunk_len = end - start
ann["text"].append(text[i : chunk_len + i])
i += chunk_len
while i < len(text) and text[i] == " ":
i += 1
else:
ann["text"] = [text]
example["text_bound_annotations"].append(ann)
elif line.startswith("E"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
ann["arguments"] = []
for role_ref_id in fields[1].split()[1:]:
argument = {
"role": (role_ref_id.split(":"))[0],
"ref_id": (role_ref_id.split(":"))[1],
}
ann["arguments"].append(argument)
example["events"].append(ann)
elif line.startswith("R"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["type"] = fields[1].split()[0]
ann["head"] = {
"role": fields[1].split()[1].split(":")[0],
"ref_id": fields[1].split()[1].split(":")[1],
}
ann["tail"] = {
"role": fields[1].split()[2].split(":")[0],
"ref_id": fields[1].split()[2].split(":")[1],
}
example["relations"].append(ann)
# '*' seems to be the legacy way to mark equivalences,
# but I couldn't find any info on the current way
# this might have to be adapted dependent on the brat version
# of the annotation
elif line.startswith("*"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["ref_ids"] = fields[1].split()[1:]
example["equivalences"].append(ann)
elif line.startswith("A") or line.startswith("M"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
info = fields[1].split()
ann["type"] = info[0]
ann["ref_id"] = info[1]
if len(info) > 2:
ann["value"] = info[2]
else:
ann["value"] = ""
example["attributes"].append(ann)
elif line.startswith("N"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["text"] = fields[2]
info = fields[1].split()
ann["type"] = info[0]
ann["ref_id"] = info[1]
ann["resource_name"] = info[2].split(":")[0]
ann["cuid"] = info[2].split(":")[1]
example["normalizations"].append(ann)
elif parse_notes and line.startswith("#"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["text"] = fields[2] if len(fields) == 3 else None
info = fields[1].split()
ann["type"] = info[0]
ann["ref_id"] = info[1]
example["notes"].append(ann)
return example
_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},
address = {Brussels, Belgium},
year = {2018},
pages = {40–46}
}
"""
_DESCRIPTION = """\
The SciArg 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.
"""
_URL = "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip"
_HOMEPAGE = "https://github.com/anlausch/ArguminSci"
@dataclass
class SciArgConfig(datasets.BuilderConfig):
data_url = _URL
subdirectory_mapping = {"compiled_corpus": datasets.Split.TRAIN}
filename_blacklist = [] #["A28"]
class SciArg(datasets.GeneratorBasedBuilder):
"""Scientific Argument corpus"""
DEFAULT_CONFIG_CLASS = SciArgConfig
BUILDER_CONFIGS = [
SciArgConfig(
name="full",
version="1.0.0",
),
]
DEFAULT_CONFIG_NAME = "full"
def _info(self) -> datasets.DatasetInfo:
features = datasets.Features(
{
"document_id": datasets.Value("string"),
"text": datasets.Value("string"),
"text_bound_annotations": [
{
"offsets": datasets.Sequence([datasets.Value("int32")]),
"text": datasets.Value("string"),
"type": datasets.Value("string"),
"id": datasets.Value("string"),
}
],
"relations": [
{
"id": datasets.Value("string"),
"head": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"tail": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"type": datasets.Value("string"),
}
],
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
data_dir = self.config.data_dir or Path(dl_manager.download_and_extract(self.config.data_url))
return [
datasets.SplitGenerator(name=split, gen_kwargs={"filepath": data_dir / subdir})
for subdir, split in self.config.subdirectory_mapping.items()
]
def _generate_examples(self, filepath):
for txt_file in glob.glob(filepath / "*.txt"):
brat_parsed = parse_brat_file(Path(txt_file))
if brat_parsed["document_id"] in self.config.filename_blacklist:
continue
relevant_subset = {f_name: brat_parsed[f_name] for f_name in self.info.features}
yield brat_parsed["document_id"], relevant_subset
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