Datasets:
ArneBinder
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3f5d38f
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Parent(s):
492fb3d
Upload sciarg.py
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sciarg.py
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@@ -0,0 +1,368 @@
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1 |
+
import glob
|
2 |
+
from dataclasses import dataclass
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3 |
+
from typing import Dict, List
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4 |
+
from pathlib import Path
|
5 |
+
|
6 |
+
import datasets
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7 |
+
|
8 |
+
|
9 |
+
def remove_prefix(a: str, prefix: str) -> str:
|
10 |
+
if a.startswith(prefix):
|
11 |
+
a = a[len(prefix) :]
|
12 |
+
return a
|
13 |
+
|
14 |
+
|
15 |
+
def parse_brat_file(
|
16 |
+
txt_file: Path,
|
17 |
+
annotation_file_suffixes: List[str] = None,
|
18 |
+
parse_notes: bool = False,
|
19 |
+
) -> Dict:
|
20 |
+
"""
|
21 |
+
Parse a brat file into the schema defined below.
|
22 |
+
`txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
|
23 |
+
Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
|
24 |
+
e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
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25 |
+
Will include annotator notes, when `parse_notes == True`.
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26 |
+
brat_features = datasets.Features(
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27 |
+
{
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28 |
+
"id": datasets.Value("string"),
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29 |
+
"document_id": datasets.Value("string"),
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30 |
+
"text": datasets.Value("string"),
|
31 |
+
"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
|
32 |
+
{
|
33 |
+
"offsets": datasets.Sequence([datasets.Value("int32")]),
|
34 |
+
"text": datasets.Sequence(datasets.Value("string")),
|
35 |
+
"type": datasets.Value("string"),
|
36 |
+
"id": datasets.Value("string"),
|
37 |
+
}
|
38 |
+
],
|
39 |
+
"events": [ # E line in brat
|
40 |
+
{
|
41 |
+
"trigger": datasets.Value(
|
42 |
+
"string"
|
43 |
+
), # refers to the text_bound_annotation of the trigger,
|
44 |
+
"id": datasets.Value("string"),
|
45 |
+
"type": datasets.Value("string"),
|
46 |
+
"arguments": datasets.Sequence(
|
47 |
+
{
|
48 |
+
"role": datasets.Value("string"),
|
49 |
+
"ref_id": datasets.Value("string"),
|
50 |
+
}
|
51 |
+
),
|
52 |
+
}
|
53 |
+
],
|
54 |
+
"relations": [ # R line in brat
|
55 |
+
{
|
56 |
+
"id": datasets.Value("string"),
|
57 |
+
"head": {
|
58 |
+
"ref_id": datasets.Value("string"),
|
59 |
+
"role": datasets.Value("string"),
|
60 |
+
},
|
61 |
+
"tail": {
|
62 |
+
"ref_id": datasets.Value("string"),
|
63 |
+
"role": datasets.Value("string"),
|
64 |
+
},
|
65 |
+
"type": datasets.Value("string"),
|
66 |
+
}
|
67 |
+
],
|
68 |
+
"equivalences": [ # Equiv line in brat
|
69 |
+
{
|
70 |
+
"id": datasets.Value("string"),
|
71 |
+
"ref_ids": datasets.Sequence(datasets.Value("string")),
|
72 |
+
}
|
73 |
+
],
|
74 |
+
"attributes": [ # M or A lines in brat
|
75 |
+
{
|
76 |
+
"id": datasets.Value("string"),
|
77 |
+
"type": datasets.Value("string"),
|
78 |
+
"ref_id": datasets.Value("string"),
|
79 |
+
"value": datasets.Value("string"),
|
80 |
+
}
|
81 |
+
],
|
82 |
+
"normalizations": [ # N lines in brat
|
83 |
+
{
|
84 |
+
"id": datasets.Value("string"),
|
85 |
+
"type": datasets.Value("string"),
|
86 |
+
"ref_id": datasets.Value("string"),
|
87 |
+
"resource_name": datasets.Value(
|
88 |
+
"string"
|
89 |
+
), # Name of the resource, e.g. "Wikipedia"
|
90 |
+
"cuid": datasets.Value(
|
91 |
+
"string"
|
92 |
+
), # ID in the resource, e.g. 534366
|
93 |
+
"text": datasets.Value(
|
94 |
+
"string"
|
95 |
+
), # Human readable description/name of the entity, e.g. "Barack Obama"
|
96 |
+
}
|
97 |
+
],
|
98 |
+
### OPTIONAL: Only included when `parse_notes == True`
|
99 |
+
"notes": [ # # lines in brat
|
100 |
+
{
|
101 |
+
"id": datasets.Value("string"),
|
102 |
+
"type": datasets.Value("string"),
|
103 |
+
"ref_id": datasets.Value("string"),
|
104 |
+
"text": datasets.Value("string"),
|
105 |
+
}
|
106 |
+
],
|
107 |
+
},
|
108 |
+
)
|
109 |
+
"""
|
110 |
+
|
111 |
+
example = {}
|
112 |
+
example["document_id"] = txt_file.with_suffix("").name
|
113 |
+
with txt_file.open() as f:
|
114 |
+
example["text"] = f.read()
|
115 |
+
|
116 |
+
# If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
|
117 |
+
# for event extraction
|
118 |
+
if annotation_file_suffixes is None:
|
119 |
+
annotation_file_suffixes = [".a1", ".a2", ".ann"]
|
120 |
+
|
121 |
+
if len(annotation_file_suffixes) == 0:
|
122 |
+
raise AssertionError(
|
123 |
+
"At least one suffix for the to-be-read annotation files should be given!"
|
124 |
+
)
|
125 |
+
|
126 |
+
ann_lines = []
|
127 |
+
for suffix in annotation_file_suffixes:
|
128 |
+
annotation_file = txt_file.with_suffix(suffix)
|
129 |
+
if annotation_file.exists():
|
130 |
+
with annotation_file.open() as f:
|
131 |
+
ann_lines.extend(f.readlines())
|
132 |
+
|
133 |
+
example["text_bound_annotations"] = []
|
134 |
+
example["events"] = []
|
135 |
+
example["relations"] = []
|
136 |
+
example["equivalences"] = []
|
137 |
+
example["attributes"] = []
|
138 |
+
example["normalizations"] = []
|
139 |
+
|
140 |
+
if parse_notes:
|
141 |
+
example["notes"] = []
|
142 |
+
|
143 |
+
for line in ann_lines:
|
144 |
+
line = line.strip()
|
145 |
+
if not line:
|
146 |
+
continue
|
147 |
+
|
148 |
+
if line.startswith("T"): # Text bound
|
149 |
+
ann = {}
|
150 |
+
fields = line.split("\t")
|
151 |
+
|
152 |
+
ann["id"] = fields[0]
|
153 |
+
ann["type"] = fields[1].split()[0]
|
154 |
+
ann["offsets"] = []
|
155 |
+
span_str = remove_prefix(fields[1], (ann["type"] + " "))
|
156 |
+
text = fields[2]
|
157 |
+
for span in span_str.split(";"):
|
158 |
+
start, end = span.split()
|
159 |
+
ann["offsets"].append([int(start), int(end)])
|
160 |
+
|
161 |
+
# Heuristically split text of discontiguous entities into chunks
|
162 |
+
ann["text"] = []
|
163 |
+
if len(ann["offsets"]) > 1:
|
164 |
+
i = 0
|
165 |
+
for start, end in ann["offsets"]:
|
166 |
+
chunk_len = end - start
|
167 |
+
ann["text"].append(text[i : chunk_len + i])
|
168 |
+
i += chunk_len
|
169 |
+
while i < len(text) and text[i] == " ":
|
170 |
+
i += 1
|
171 |
+
else:
|
172 |
+
ann["text"] = [text]
|
173 |
+
|
174 |
+
example["text_bound_annotations"].append(ann)
|
175 |
+
|
176 |
+
elif line.startswith("E"):
|
177 |
+
ann = {}
|
178 |
+
fields = line.split("\t")
|
179 |
+
|
180 |
+
ann["id"] = fields[0]
|
181 |
+
|
182 |
+
ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
|
183 |
+
|
184 |
+
ann["arguments"] = []
|
185 |
+
for role_ref_id in fields[1].split()[1:]:
|
186 |
+
argument = {
|
187 |
+
"role": (role_ref_id.split(":"))[0],
|
188 |
+
"ref_id": (role_ref_id.split(":"))[1],
|
189 |
+
}
|
190 |
+
ann["arguments"].append(argument)
|
191 |
+
|
192 |
+
example["events"].append(ann)
|
193 |
+
|
194 |
+
elif line.startswith("R"):
|
195 |
+
ann = {}
|
196 |
+
fields = line.split("\t")
|
197 |
+
|
198 |
+
ann["id"] = fields[0]
|
199 |
+
ann["type"] = fields[1].split()[0]
|
200 |
+
|
201 |
+
ann["head"] = {
|
202 |
+
"role": fields[1].split()[1].split(":")[0],
|
203 |
+
"ref_id": fields[1].split()[1].split(":")[1],
|
204 |
+
}
|
205 |
+
ann["tail"] = {
|
206 |
+
"role": fields[1].split()[2].split(":")[0],
|
207 |
+
"ref_id": fields[1].split()[2].split(":")[1],
|
208 |
+
}
|
209 |
+
|
210 |
+
example["relations"].append(ann)
|
211 |
+
|
212 |
+
# '*' seems to be the legacy way to mark equivalences,
|
213 |
+
# but I couldn't find any info on the current way
|
214 |
+
# this might have to be adapted dependent on the brat version
|
215 |
+
# of the annotation
|
216 |
+
elif line.startswith("*"):
|
217 |
+
ann = {}
|
218 |
+
fields = line.split("\t")
|
219 |
+
|
220 |
+
ann["id"] = fields[0]
|
221 |
+
ann["ref_ids"] = fields[1].split()[1:]
|
222 |
+
|
223 |
+
example["equivalences"].append(ann)
|
224 |
+
|
225 |
+
elif line.startswith("A") or line.startswith("M"):
|
226 |
+
ann = {}
|
227 |
+
fields = line.split("\t")
|
228 |
+
|
229 |
+
ann["id"] = fields[0]
|
230 |
+
|
231 |
+
info = fields[1].split()
|
232 |
+
ann["type"] = info[0]
|
233 |
+
ann["ref_id"] = info[1]
|
234 |
+
|
235 |
+
if len(info) > 2:
|
236 |
+
ann["value"] = info[2]
|
237 |
+
else:
|
238 |
+
ann["value"] = ""
|
239 |
+
|
240 |
+
example["attributes"].append(ann)
|
241 |
+
|
242 |
+
elif line.startswith("N"):
|
243 |
+
ann = {}
|
244 |
+
fields = line.split("\t")
|
245 |
+
|
246 |
+
ann["id"] = fields[0]
|
247 |
+
ann["text"] = fields[2]
|
248 |
+
|
249 |
+
info = fields[1].split()
|
250 |
+
|
251 |
+
ann["type"] = info[0]
|
252 |
+
ann["ref_id"] = info[1]
|
253 |
+
ann["resource_name"] = info[2].split(":")[0]
|
254 |
+
ann["cuid"] = info[2].split(":")[1]
|
255 |
+
example["normalizations"].append(ann)
|
256 |
+
|
257 |
+
elif parse_notes and line.startswith("#"):
|
258 |
+
ann = {}
|
259 |
+
fields = line.split("\t")
|
260 |
+
|
261 |
+
ann["id"] = fields[0]
|
262 |
+
ann["text"] = fields[2] if len(fields) == 3 else None
|
263 |
+
|
264 |
+
info = fields[1].split()
|
265 |
+
|
266 |
+
ann["type"] = info[0]
|
267 |
+
ann["ref_id"] = info[1]
|
268 |
+
example["notes"].append(ann)
|
269 |
+
|
270 |
+
return example
|
271 |
+
|
272 |
+
|
273 |
+
_CITATION = """\
|
274 |
+
@inproceedings{lauscher2018b,
|
275 |
+
title = {An argument-annotated corpus of scientific publications},
|
276 |
+
booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
|
277 |
+
publisher = {Association for Computational Linguistics},
|
278 |
+
author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
|
279 |
+
address = {Brussels, Belgium},
|
280 |
+
year = {2018},
|
281 |
+
pages = {40–46}
|
282 |
+
}
|
283 |
+
"""
|
284 |
+
_DESCRIPTION = """\
|
285 |
+
The SciArg dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing
|
286 |
+
fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific
|
287 |
+
publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of
|
288 |
+
scientific writing.
|
289 |
+
"""
|
290 |
+
_URL = "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip"
|
291 |
+
_HOMEPAGE = "https://github.com/anlausch/ArguminSci"
|
292 |
+
|
293 |
+
|
294 |
+
@dataclass
|
295 |
+
class SciArgConfig(datasets.BuilderConfig):
|
296 |
+
data_url = _URL
|
297 |
+
subdirectory_mapping = {"compiled_corpus": datasets.Split.TRAIN}
|
298 |
+
filename_blacklist = [] #["A28"]
|
299 |
+
|
300 |
+
|
301 |
+
class SciArg(datasets.GeneratorBasedBuilder):
|
302 |
+
"""Scientific Argument corpus"""
|
303 |
+
|
304 |
+
DEFAULT_CONFIG_CLASS = SciArgConfig
|
305 |
+
|
306 |
+
BUILDER_CONFIGS = [
|
307 |
+
SciArgConfig(
|
308 |
+
name="full",
|
309 |
+
version="1.0.0",
|
310 |
+
),
|
311 |
+
]
|
312 |
+
|
313 |
+
DEFAULT_CONFIG_NAME = "full"
|
314 |
+
|
315 |
+
def _info(self) -> datasets.DatasetInfo:
|
316 |
+
features = datasets.Features(
|
317 |
+
{
|
318 |
+
"document_id": datasets.Value("string"),
|
319 |
+
"text": datasets.Value("string"),
|
320 |
+
"text_bound_annotations": [
|
321 |
+
{
|
322 |
+
"offsets": datasets.Sequence([datasets.Value("int32")]),
|
323 |
+
"text": datasets.Value("string"),
|
324 |
+
"type": datasets.Value("string"),
|
325 |
+
"id": datasets.Value("string"),
|
326 |
+
}
|
327 |
+
],
|
328 |
+
"relations": [
|
329 |
+
{
|
330 |
+
"id": datasets.Value("string"),
|
331 |
+
"head": {
|
332 |
+
"ref_id": datasets.Value("string"),
|
333 |
+
"role": datasets.Value("string"),
|
334 |
+
},
|
335 |
+
"tail": {
|
336 |
+
"ref_id": datasets.Value("string"),
|
337 |
+
"role": datasets.Value("string"),
|
338 |
+
},
|
339 |
+
"type": datasets.Value("string"),
|
340 |
+
}
|
341 |
+
],
|
342 |
+
}
|
343 |
+
)
|
344 |
+
|
345 |
+
return datasets.DatasetInfo(
|
346 |
+
description=_DESCRIPTION,
|
347 |
+
features=features,
|
348 |
+
homepage=_HOMEPAGE,
|
349 |
+
citation=_CITATION,
|
350 |
+
)
|
351 |
+
|
352 |
+
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
|
353 |
+
"""Returns SplitGenerators."""
|
354 |
+
data_dir = self.config.data_dir or Path(dl_manager.download_and_extract(self.config.data_url))
|
355 |
+
|
356 |
+
return [
|
357 |
+
datasets.SplitGenerator(name=split, gen_kwargs={"filepath": data_dir / subdir})
|
358 |
+
for subdir, split in self.config.subdirectory_mapping.items()
|
359 |
+
]
|
360 |
+
|
361 |
+
def _generate_examples(self, filepath):
|
362 |
+
for txt_file in glob.glob(filepath / "*.txt"):
|
363 |
+
|
364 |
+
brat_parsed = parse_brat_file(Path(txt_file))
|
365 |
+
if brat_parsed["document_id"] in self.config.filename_blacklist:
|
366 |
+
continue
|
367 |
+
relevant_subset = {f_name: brat_parsed[f_name] for f_name in self.info.features}
|
368 |
+
yield brat_parsed["document_id"], relevant_subset
|