Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 21,331 Bytes
ad98ea1 98d425e ad98ea1 bf15869 ad98ea1 98d425e ad98ea1 98d425e ad98ea1 98d425e ad98ea1 98d425e bf15869 98d425e ad98ea1 ac29f28 ad98ea1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""
import glob
import os
import pandas as pd
import datasets
_CITATION = """\
@misc{friedrich2020sofcexp,
title={The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain},
author={Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange},
year={2020},
eprint={2006.03039},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
The SOFC-Exp corpus consists of 45 open-access scholarly articles annotated by domain experts.
A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested
named entity recognition and slot filling tasks as well as high annotation quality is presented
in the accompanying paper.
"""
_HOMEPAGE = "https://arxiv.org/abs/2006.03039"
_LICENSE = ""
_URL = "data.zip"
class SOFCMaterialsArticles(datasets.GeneratorBasedBuilder):
""" """
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"text": datasets.Value("string"),
"sentence_offsets": datasets.features.Sequence(
{"begin_char_offset": datasets.Value("int64"), "end_char_offset": datasets.Value("int64")}
),
"sentences": datasets.features.Sequence(datasets.Value("string")),
"sentence_labels": datasets.features.Sequence(datasets.Value("int64")),
"token_offsets": datasets.features.Sequence(
{
"offsets": datasets.features.Sequence(
{"begin_char_offset": datasets.Value("int64"), "end_char_offset": datasets.Value("int64")}
)
}
),
"tokens": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
"entity_labels": datasets.features.Sequence(
datasets.features.Sequence(
datasets.features.ClassLabel(
names=[
"B-DEVICE",
"B-EXPERIMENT",
"B-MATERIAL",
"B-VALUE",
"I-DEVICE",
"I-EXPERIMENT",
"I-MATERIAL",
"I-VALUE",
"O",
]
)
)
),
"slot_labels": datasets.features.Sequence(
datasets.features.Sequence(
datasets.features.ClassLabel(
names=[
"B-anode_material",
"B-cathode_material",
"B-conductivity",
"B-current_density",
"B-degradation_rate",
"B-device",
"B-electrolyte_material",
"B-experiment_evoking_word",
"B-fuel_used",
"B-interlayer_material",
"B-interconnect_material",
"B-open_circuit_voltage",
"B-power_density",
"B-resistance",
"B-support_material",
"B-thickness",
"B-time_of_operation",
"B-voltage",
"B-working_temperature",
"I-anode_material",
"I-cathode_material",
"I-conductivity",
"I-current_density",
"I-degradation_rate",
"I-device",
"I-electrolyte_material",
"I-experiment_evoking_word",
"I-fuel_used",
"I-interlayer_material",
"I-interconnect_material",
"I-open_circuit_voltage",
"I-power_density",
"I-resistance",
"I-support_material",
"I-thickness",
"I-time_of_operation",
"I-voltage",
"I-working_temperature",
"O",
]
)
)
),
"links": datasets.Sequence(
{
"relation_label": datasets.features.ClassLabel(
names=["coreference", "experiment_variation", "same_experiment", "thickness"]
),
"start_span_id": datasets.Value("int64"),
"end_span_id": datasets.Value("int64"),
}
),
"slots": datasets.features.Sequence(
{
"frame_participant_label": datasets.features.ClassLabel(
names=[
"anode_material",
"cathode_material",
"current_density",
"degradation_rate",
"device",
"electrolyte_material",
"fuel_used",
"interlayer_material",
"open_circuit_voltage",
"power_density",
"resistance",
"support_material",
"time_of_operation",
"voltage",
"working_temperature",
]
),
"slot_id": datasets.Value("int64"),
}
),
"spans": datasets.features.Sequence(
{
"span_id": datasets.Value("int64"),
"entity_label": datasets.features.ClassLabel(names=["", "DEVICE", "MATERIAL", "VALUE"]),
"sentence_id": datasets.Value("int64"),
"experiment_mention_type": datasets.features.ClassLabel(
names=["", "current_exp", "future_work", "general_info", "previous_work"]
),
"begin_char_offset": datasets.Value("int64"),
"end_char_offset": datasets.Value("int64"),
}
),
"experiments": datasets.features.Sequence(
{
"experiment_id": datasets.Value("int64"),
"span_id": datasets.Value("int64"),
"slots": datasets.features.Sequence(
{
"frame_participant_label": datasets.features.ClassLabel(
names=[
"anode_material",
"cathode_material",
"current_density",
"degradation_rate",
"conductivity",
"device",
"electrolyte_material",
"fuel_used",
"interlayer_material",
"open_circuit_voltage",
"power_density",
"resistance",
"support_material",
"time_of_operation",
"voltage",
"working_temperature",
]
),
"slot_id": datasets.Value("int64"),
}
),
}
),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URL)
data_dir = os.path.join(data_dir, "sofc-exp-corpus")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data_dir": data_dir,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data_dir": data_dir,
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data_dir": data_dir,
"split": "dev",
},
),
]
def _generate_examples(self, data_dir, split):
"""Yields examples."""
metadata = pd.read_csv(os.path.join(data_dir, "SOFC-Exp-Metadata.csv"), sep="\t")
names = metadata[metadata["set"] == split]["name"].tolist()
# The dataset consists of the original article text as well as annotations
textfile_base_path = os.path.join(data_dir, "texts")
annotations_base_path = os.path.join(data_dir, "annotations")
# The annotations are mostly references to offsets in the source text
# with corresponding labels, so we'll refer to them as `meta`
sentence_meta_base_path = os.path.join(annotations_base_path, "sentences")
tokens_meta_base_path = os.path.join(annotations_base_path, "tokens")
ets_meta_base_path = os.path.join(annotations_base_path, "entity_types_and_slots")
frame_meta_base_path = os.path.join(annotations_base_path, "frames")
# Define the headers for the sentence and token and entity metadata
sentence_meta_header = ["sentence_id", "label", "begin_char_offset", "end_char_offset"]
tokens_meta_header = ["sentence_id", "token_id", "begin_char_offset", "end_char_offset"]
ets_meta_header = [
"sentence_id",
"token_id",
"begin_char_offset",
"end_char_offset",
"entity_label",
"slot_label",
]
# Start the processing loop
# For each text file, we'll load all of the
# associated annotation files
for id_, name in enumerate(sorted(names)):
# Load the main source text
textfile_path = os.path.join(textfile_base_path, name + ".txt")
text = open(textfile_path, encoding="utf-8").read()
# Load the sentence offsets file
sentence_meta_path = os.path.join(sentence_meta_base_path, name + ".csv")
sentence_meta = pd.read_csv(sentence_meta_path, sep="\t", names=sentence_meta_header)
# Load the tokens offsets file
tokens_meta_path = os.path.join(tokens_meta_base_path, name + ".csv")
tokens_meta = pd.read_csv(tokens_meta_path, sep="\t", names=tokens_meta_header)
# Load the entity offsets file
ets_meta_path = os.path.join(ets_meta_base_path, name + ".csv")
ets_meta = pd.read_csv(ets_meta_path, sep="\t", names=ets_meta_header)
# Create a list of lists indexed as [sentence][token] for the entity and slot labels
entity_labels = ets_meta.groupby("sentence_id").apply(lambda x: x["entity_label"].tolist()).to_list()
slot_labels = ets_meta.groupby("sentence_id").apply(lambda x: x["slot_label"].tolist()).to_list()
# Create a list of lists for the token offsets indexed as [sentence][token]
# Each element will contain a dict with beginning and ending character offsets
token_offsets = (
tokens_meta.groupby("sentence_id")[["begin_char_offset", "end_char_offset"]]
.apply(lambda x: x.to_dict(orient="records"))
.tolist()
)
# Load the frames metadata. The frames file contains the data for all of the annotations
# in a condensed format that varies throughout the file. More information on this format
# can be found: https://framenet.icsi.berkeley.edu/fndrupal/
frames_meta_path = os.path.join(frame_meta_base_path, name + ".csv")
frames_meta = open(frames_meta_path, encoding="utf-8").readlines()
# Parse the sentence offsets, producing a list of dicts with the
# starting and ending position of each sentence in the original text
sentence_offsets = (
sentence_meta[["begin_char_offset", "end_char_offset"]].apply(lambda x: x.to_dict(), axis=1).tolist()
)
# The sentence labels are a binary label that describes whether the sentence contains
# any annotations
sentence_labels = sentence_meta["label"].tolist()
# Materialiaze a list of strings of the actual sentences
sentences = [text[ost["begin_char_offset"] : ost["end_char_offset"]] for ost in sentence_offsets]
# Materialize a list of lists of the tokens in each sentence.
# Annotation labels are aligned with these tokens, so be careful with
# alignment if using your own tokenization scheme with the sentences above
tokens = [
[s[tto["begin_char_offset"] : tto["end_char_offset"]] for tto in to]
for s, to in zip(sentences, token_offsets)
]
# The frames file first contains spans annotations (in one format),
# then contains experiments annotations (in another format),
# then links annotations (in yet another format).
# Here we find the beginning of the experiments and links sections of the file
# Additionally, each experiment annotation in the experiment annotations begins with a
# line starting with the word EXPERIMENT (in one format)
# followed by the annotations for that experiment (in yet _another_ format)
# Here we get the start positions for each experiment _within_ the experiments
# section of the frames data
experiment_starts = [i for i, line in enumerate(frames_meta) if line.startswith("EXPERIMENT")]
experiment_start = min(experiment_starts)
link_start = min([i for i, line in enumerate(frames_meta) if line.startswith("LINK")])
# Pick out the spans section of the data for parsing
spans_raw = frames_meta[:experiment_start]
# Iterate through the spans data
spans = []
for span in spans_raw:
# Split out the elements in each tab-delimited line
_, span_id, entity_label_or_exp, sentence_id, begin_char_offset, end_char_offset = span.split("\t")
# The entity label for experiment spans have a sub-label,
# called the experiment mention type,
# which is sub-delimited by a ':'
# The code below standardizes the fields produced by
# each line to a common schema, some fields of which may
# be empty depending on the data available in the line
if entity_label_or_exp.startswith("EXPERIMENT"):
exp, experiment_mention_type = entity_label_or_exp.split(":")
entity_label = ""
else:
entity_label = entity_label_or_exp
exp = ""
experiment_mention_type = ""
s = {
"span_id": span_id,
"entity_label": entity_label,
"sentence_id": sentence_id,
"experiment_mention_type": experiment_mention_type,
"begin_char_offset": int(begin_char_offset),
"end_char_offset": int(end_char_offset),
}
spans.append(s)
# Pull out the links annotations for from the frames data
links_raw = [f.rstrip("\n") for f in frames_meta[link_start:]]
# Iterate through the links data, which is in a simple tab-delimited format
links = []
for link in links_raw:
_, relation_label, start_span_id, end_span_id = link.split("\t")
link_out = {
"relation_label": relation_label,
"start_span_id": int(start_span_id),
"end_span_id": int(end_span_id),
}
links.append(link_out)
# Iterate through the experiments data and parse each experiment
experiments = []
# Zip the experiment start offsets to get start/end position tuples
# for each experiment in the experiments data
for start, end in zip(experiment_starts[:-1], experiment_starts[1:]):
current_experiment = frames_meta[start:end]
# The first line of each experiment annotation contains the
# experiment id and the span id
_, experiment_id, span_id = current_experiment[0].rstrip("\n").split("\t")
exp = {"experiment_id": int(experiment_id), "span_id": int(span_id)}
# The remaining lines in the experiment annotations contain
# slot level information for each experiment.
slots = []
for e in current_experiment[1:]:
e = e.rstrip("\n")
_, frame_participant_label, slot_id = e.split("\t")
to_add = {"frame_participant_label": frame_participant_label, "slot_id": int(slot_id)}
slots.append(to_add)
exp["slots"] = slots
experiments.append(exp)
# Yield the final parsed example output
# NOTE: the `token_offsets` is converted to a list of
# dicts to accommodate processing to the arrow files
# in the `features` schema defined above
yield id_, {
"text": text,
"sentence_offsets": sentence_offsets,
"sentences": sentences,
"sentence_labels": sentence_labels,
"token_offsets": [{"offsets": to} for to in token_offsets],
"tokens": tokens,
"entity_labels": entity_labels,
"slot_labels": slot_labels,
"links": links,
"slots": slots,
"spans": spans,
"experiments": experiments,
}
|