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timo-pierre-schrader
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- COPYRIGHT +0 -14
- LICENSE_CORPUS +0 -428
- README.md +175 -174
- teaser.png +0 -3
COPYRIGHT
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Experiment resources related to the MuLMS corpus.
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Copyright (c) 2023 Robert Bosch GmbH
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU Affero General Public License as published
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by the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU Affero General Public License for more details.
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You should have received a copy of the GNU Affero General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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LICENSE_CORPUS
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Attribution-ShareAlike 4.0 International
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In addition to the conditions in Section 3(a), if You Share
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Adapted Material You produce, the following conditions also apply.
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3. You may not offer or impose any additional or different terms
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Section 4 -- Sui Generis Database Rights.
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Where the Licensed Rights include Sui Generis Database Rights that
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a. for the avoidance of doubt, Section 2(a)(1) grants You the right
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portion of the contents of the database;
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For the avoidance of doubt, this Section 4 supplements and does not
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replace Your obligations under this Public License where the Licensed
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Rights include other Copyright and Similar Rights.
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Section 5 -- Disclaimer of Warranties and Limitation of Liability.
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a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
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EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
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KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
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ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
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b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
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INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES,
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USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN
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ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
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IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
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c. The disclaimer of warranties and limitation of liability provided
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above shall be interpreted in a manner that, to the extent
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possible, most closely approximates an absolute disclaimer and
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waiver of all liability.
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Section 6 -- Term and Termination.
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a. This Public License applies for the term of the Copyright and
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Similar Rights licensed here. However, if You fail to comply with
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terminate automatically.
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b. Where Your right to use the Licensed Material has terminated under
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Section 6(a), it reinstates:
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1. automatically as of the date the violation is cured, provided
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it is cured within 30 days of Your discovery of the
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violation; or
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2. upon express reinstatement by the Licensor.
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For the avoidance of doubt, this Section 6(b) does not affect any
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right the Licensor may have to seek remedies for Your violations
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of this Public License.
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c. For the avoidance of doubt, the Licensor may also offer the
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distributing the Licensed Material at any time; however, doing so
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d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
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Section 7 -- Other Terms and Conditions.
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a. The Licensor shall not be bound by any additional or different
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Licensed Material not stated herein are separate from and
|
381 |
-
independent of the terms and conditions of this Public License.
|
382 |
-
|
383 |
-
|
384 |
-
Section 8 -- Interpretation.
|
385 |
-
|
386 |
-
a. For the avoidance of doubt, this Public License does not, and
|
387 |
-
shall not be interpreted to, reduce, limit, restrict, or impose
|
388 |
-
conditions on any use of the Licensed Material that could lawfully
|
389 |
-
be made without permission under this Public License.
|
390 |
-
|
391 |
-
b. To the extent possible, if any provision of this Public License is
|
392 |
-
deemed unenforceable, it shall be automatically reformed to the
|
393 |
-
minimum extent necessary to make it enforceable. If the provision
|
394 |
-
cannot be reformed, it shall be severed from this Public License
|
395 |
-
without affecting the enforceability of the remaining terms and
|
396 |
-
conditions.
|
397 |
-
|
398 |
-
c. No term or condition of this Public License will be waived and no
|
399 |
-
failure to comply consented to unless expressly agreed to by the
|
400 |
-
Licensor.
|
401 |
-
|
402 |
-
d. Nothing in this Public License constitutes or may be interpreted
|
403 |
-
as a limitation upon, or waiver of, any privileges and immunities
|
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-
that apply to the Licensor or You, including from the legal
|
405 |
-
processes of any jurisdiction or authority.
|
406 |
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|
407 |
-
|
408 |
-
=======================================================================
|
409 |
-
|
410 |
-
Creative Commons is not a party to its public
|
411 |
-
licenses. Notwithstanding, Creative Commons may elect to apply one of
|
412 |
-
its public licenses to material it publishes and in those instances
|
413 |
-
will be considered the “Licensor.†The text of the Creative Commons
|
414 |
-
public licenses is dedicated to the public domain under the CC0 Public
|
415 |
-
Domain Dedication. Except for the limited purpose of indicating that
|
416 |
-
material is shared under a Creative Commons public license or as
|
417 |
-
otherwise permitted by the Creative Commons policies published at
|
418 |
-
creativecommons.org/policies, Creative Commons does not authorize the
|
419 |
-
use of the trademark "Creative Commons" or any other trademark or logo
|
420 |
-
of Creative Commons without its prior written consent including,
|
421 |
-
without limitation, in connection with any unauthorized modifications
|
422 |
-
to any of its public licenses or any other arrangements,
|
423 |
-
understandings, or agreements concerning use of licensed material. For
|
424 |
-
the avoidance of doubt, this paragraph does not form part of the
|
425 |
-
public licenses.
|
426 |
-
|
427 |
-
Creative Commons may be contacted at creativecommons.org.
|
428 |
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|
README.md
CHANGED
@@ -1,122 +1,122 @@
|
|
1 |
---
|
2 |
license: cc-by-sa-4.0
|
3 |
language:
|
4 |
-
|
5 |
size_categories:
|
6 |
-
|
7 |
source_datasets:
|
8 |
-
|
9 |
task_categories:
|
10 |
-
|
11 |
-
|
12 |
-
|
13 |
task_ids:
|
14 |
-
|
15 |
-
|
16 |
pretty_name: Multi-Layer Materials Science Corpus
|
17 |
configs:
|
18 |
-
|
19 |
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|
20 |
-
|
21 |
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|
22 |
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23 |
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|
30 |
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|
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|
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|
33 |
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|
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|
35 |
dataset_info:
|
36 |
-
|
37 |
-
|
38 |
-
|
39 |
-
|
40 |
-
|
41 |
-
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|
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|
116 |
-
|
117 |
-
|
118 |
-
|
119 |
-
|
120 |
---
|
121 |
|
122 |
# Dataset Card for MuLMS
|
@@ -128,17 +128,17 @@ dataset_info:
|
|
128 |
|
129 |
### Dataset Description
|
130 |
|
131 |
-
The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas:
|
132 |
-
"Electrolysis", "Graphene", "Polymer Electrolyte Fuel Cell (PEMFC)", "Solid Oxide Fuel Cell (SOFC)", "Polymers", "Semiconductors" and "Steel".
|
133 |
It was exhaustively annotated by domain experts. There are annotations on sentence-level and token-level for the following NLP tasks:
|
134 |
|
135 |
-
|
136 |
-
|
137 |
-
|
138 |
-
|
139 |
-
|
140 |
-
|
141 |
-
|
142 |
zones in MuLMS, which leads to a sentence-level classification task.
|
143 |
|
144 |
You can find all experiment code files and further information in the [MuLMS-AZ Repo](https://github.com/boschresearch/mulms-az-codi2023) and [MuLMS Repo](https://github.com/boschresearch/mulms-wiesp2023).
|
@@ -153,6 +153,8 @@ For dataset statistics, please refer to both papers listed below. There you can
|
|
153 |
|
154 |
MuLMS provides all annotated files in UIMA CAS XMI format that can be used with annotation tools that can read these files such as [INCEpTION](https://inception-project.github.io/).
|
155 |
|
|
|
|
|
156 |
### Dataset Sources
|
157 |
|
158 |
<!-- Provide the basic links for the dataset. -->
|
@@ -209,7 +211,7 @@ HuggingFace datasets do not support these sub-splits, hence they must be loaded
|
|
209 |
|
210 |
Each instance in this config refers to one token and carries a copy of the entire sentence, i.e., for _n_ tokens in a sentence, the text of the sentence is given _n_ times.
|
211 |
|
212 |
-
- `index`: Unique instance ID for each token.
|
213 |
- `ID`: Sentence ID. As opposed to the other config, the sentences here are not sorted by document and provided in their full form for every token they belong to.
|
214 |
- `Sentence`: Sentence string
|
215 |
- `Token_ID`: Unique ID for each token within each sentence. ID is resetted for each new sentence.
|
@@ -222,52 +224,52 @@ Each instance in this config refers to one token and carries a copy of the entir
|
|
222 |
|
223 |
For the different layers, the following labels are available:
|
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 |
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|
251 |
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|
252 |
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|
253 |
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|
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|
255 |
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|
256 |
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|
257 |
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|
258 |
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|
259 |
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|
260 |
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|
261 |
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|
262 |
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|
263 |
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|
264 |
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|
265 |
-
|
266 |
-
|
267 |
-
|
268 |
-
|
269 |
-
|
270 |
-
|
271 |
|
272 |
## Dataset Creation
|
273 |
|
@@ -293,7 +295,7 @@ You can find all the authors for every document in this corpus in [MuLMS_Corpus_
|
|
293 |
|
294 |
#### Annotation process
|
295 |
|
296 |
-
The annotation process included guideline design in dedicated discussion sessions. Afterward, the text files were annotated
|
297 |
using [INCEpTION](https://inception-project.github.io/).
|
298 |
|
299 |
#### Who are the annotators?
|
@@ -313,10 +315,9 @@ This dataset does not contain any personal, sensitive or private data. MuLMS bui
|
|
313 |
If you use our software or dataset in your scientific work, please cite both papers:
|
314 |
|
315 |
**BibTeX:**
|
316 |
-
|
317 |
```
|
318 |
@misc{schrader2023mulms,
|
319 |
-
title={MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain},
|
320 |
author={Timo Pierre Schrader and Matteo Finco and Stefan Grünewald and Felix Hildebrand and Annemarie Friedrich},
|
321 |
year={2023},
|
322 |
eprint={2310.15569},
|
@@ -377,12 +378,12 @@ The MuLMS corpus is released under the [CC BY-SA 4.0](https://creativecommons.or
|
|
377 |
|
378 |
## Dataset Card Authors
|
379 |
|
380 |
-
|
381 |
-
|
382 |
-
|
383 |
-
|
384 |
-
|
385 |
|
386 |
## Dataset Card Contact
|
387 |
|
388 |
-
For all questions, please contact [Timo Schrader](mailto:[email protected]).
|
|
|
1 |
---
|
2 |
license: cc-by-sa-4.0
|
3 |
language:
|
4 |
+
- en
|
5 |
size_categories:
|
6 |
+
- 10K<n<100K
|
7 |
source_datasets:
|
8 |
+
- original
|
9 |
task_categories:
|
10 |
+
- fill-mask
|
11 |
+
- token-classification
|
12 |
+
- text-classification
|
13 |
task_ids:
|
14 |
+
- named-entity-recognition
|
15 |
+
- slot-filling
|
16 |
pretty_name: Multi-Layer Materials Science Corpus
|
17 |
configs:
|
18 |
+
- config_name: MuLMS_Corpus
|
19 |
+
data_files:
|
20 |
+
- split: train
|
21 |
+
path: MuLMS_Corpus/train-*
|
22 |
+
- split: validation
|
23 |
+
path: MuLMS_Corpus/validation-*
|
24 |
+
- split: test
|
25 |
+
path: MuLMS_Corpus/test-*
|
26 |
+
default: true
|
27 |
+
- config_name: NER_Dependencies
|
28 |
+
data_files:
|
29 |
+
- split: train
|
30 |
+
path: NER_Dependencies/train-*
|
31 |
+
- split: validation
|
32 |
+
path: NER_Dependencies/validation-*
|
33 |
+
- split: test
|
34 |
+
path: NER_Dependencies/test-*
|
35 |
dataset_info:
|
36 |
+
- config_name: MuLMS_Corpus
|
37 |
+
features:
|
38 |
+
- name: doc_id
|
39 |
+
dtype: string
|
40 |
+
- name: sentence
|
41 |
+
dtype: string
|
42 |
+
- name: tokens
|
43 |
+
sequence: string
|
44 |
+
- name: beginOffset
|
45 |
+
dtype: int32
|
46 |
+
- name: endOffset
|
47 |
+
dtype: int32
|
48 |
+
- name: AZ_labels
|
49 |
+
dtype: string
|
50 |
+
- name: Measurement_label
|
51 |
+
dtype: string
|
52 |
+
- name: NER_labels
|
53 |
+
sequence:
|
54 |
+
- name: text
|
55 |
+
dtype: string
|
56 |
+
- name: id
|
57 |
+
dtype: int32
|
58 |
+
- name: value
|
59 |
+
dtype: string
|
60 |
+
- name: begin
|
61 |
+
dtype: string
|
62 |
+
- name: end
|
63 |
+
dtype: string
|
64 |
+
- name: tokenIndices
|
65 |
+
sequence: int32
|
66 |
+
- name: NER_labels_BILOU
|
67 |
+
sequence: string
|
68 |
+
- name: relations
|
69 |
+
sequence:
|
70 |
+
- name: ne_id_gov
|
71 |
+
dtype: int32
|
72 |
+
- name: ne_id_dep
|
73 |
+
dtype: int32
|
74 |
+
- name: label
|
75 |
+
dtype: string
|
76 |
+
- name: docFileName
|
77 |
+
dtype: string
|
78 |
+
- name: data_split
|
79 |
+
dtype: string
|
80 |
+
- name: category
|
81 |
+
dtype: string
|
82 |
+
splits:
|
83 |
+
- name: train
|
84 |
+
num_bytes: 7319898
|
85 |
+
num_examples: 7538
|
86 |
+
- name: validation
|
87 |
+
num_bytes: 1499121
|
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- config_name: NER_Dependencies
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features:
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- name: ID
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dtype: int32
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- name: sentence
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dtype: string
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dtype: int32
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- name: token_text
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dtype: string
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- name: NE_Dependencies
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dtype: string
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- name: data_split
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dtype: string
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splits:
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- name: train
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- name: validation
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num_bytes: 9320669
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num_examples: 42010
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- name: test
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dataset_size: 68288938
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---
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# Dataset Card for MuLMS
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### Dataset Description
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The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas:
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"Electrolysis", "Graphene", "Polymer Electrolyte Fuel Cell (PEMFC)", "Solid Oxide Fuel Cell (SOFC)", "Polymers", "Semiconductors" and "Steel".
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It was exhaustively annotated by domain experts. There are annotations on sentence-level and token-level for the following NLP tasks:
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* __Measurement Frames__: Measurement annotations are treated in a frame-like fashion, using the span type MEASUREMENT to mark the triggers (e.g.,
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was measured, is plotted) that introduce the Measurement frame to the discourse. Deciding whether a sentence contains a measurement trigger is treated as a
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sentence-level task, determining the span that triggers the measurement frame is treated as named entity recognition.
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* __Named Entities__: There are 12 token-level named entities (+ Measurement trigger) available in MuLMS. Named entities can span across multiple tokens.
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* __Relations__: MuLMS provides relations between pairs of entities. There are two types of relations: measurement-related relations and further relations.
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The first type always starts at Measurement trigger spans, the scond type does not start at a specific Measurement annotation.
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* __Argumentative Zones__: Each sentence in MuLMS is assigned a rhetorical function in the discourse (e.g., _Background_ or _Experiment_Preparation_). There are 12 argumentative
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zones in MuLMS, which leads to a sentence-level classification task.
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You can find all experiment code files and further information in the [MuLMS-AZ Repo](https://github.com/boschresearch/mulms-az-codi2023) and [MuLMS Repo](https://github.com/boschresearch/mulms-wiesp2023).
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MuLMS provides all annotated files in UIMA CAS XMI format that can be used with annotation tools that can read these files such as [INCEpTION](https://inception-project.github.io/).
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__Important:__ To use the dataset reader, please install the UIMA CAS Python reader _puima_ using the following command: `pip install git+https://github.com/annefried/puima.git`.
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+
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### Dataset Sources
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<!-- Provide the basic links for the dataset. -->
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Each instance in this config refers to one token and carries a copy of the entire sentence, i.e., for _n_ tokens in a sentence, the text of the sentence is given _n_ times.
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- `index`: Unique instance ID for each token.
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- `ID`: Sentence ID. As opposed to the other config, the sentences here are not sorted by document and provided in their full form for every token they belong to.
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- `Sentence`: Sentence string
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- `Token_ID`: Unique ID for each token within each sentence. ID is resetted for each new sentence.
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For the different layers, the following labels are available:
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* __Measurement Frames__:
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* `Measurement`
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* `Qual_Measurement`
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* __Named Entities__:
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* `MAT`
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* `NUM`
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* `VALUE`
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* `UNIT`
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* `PROPERTY`
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* `FORM`
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* `MEASUREMENT` (measurement frame-evoking trigger)
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* `CITE`
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* `SAMPLE`
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* `TECHNIQUE`
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* `DEV`
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* `RANGE`
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* `INSTRUMENT`
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* __Relations__:
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* `hasForm`
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* `measuresProperty`
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* `usedAs`
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* `propertyValue`
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* `conditionProperty`
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* `conditionSample`
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* `conditionPropertyValue`
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* `usesTechnique`
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* `measuresPropertyValue`
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* `usedTogether`
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* `conditionEnv`
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* `usedIn`
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* `conditionInstrument`
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* `takenFrom`
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* `dopedBy`
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* __Argumentative Zones__:
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* `Motivation`
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* `Background`
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* `PriorWork`
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* `Experiment`
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* `Preparation`
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* `Characterization`
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* `Explanation`
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* `Results`
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* `Conclusion`
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* `Heading`
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* `Caption`
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* `Metadata`
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## Dataset Creation
|
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|
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#### Annotation process
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The annotation process included guideline design in dedicated discussion sessions. Afterward, the text files were annotated
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using [INCEpTION](https://inception-project.github.io/).
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#### Who are the annotators?
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If you use our software or dataset in your scientific work, please cite both papers:
|
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|
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**BibTeX:**
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|
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```
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@misc{schrader2023mulms,
|
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+
title={MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain},
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author={Timo Pierre Schrader and Matteo Finco and Stefan Grünewald and Felix Hildebrand and Annemarie Friedrich},
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year={2023},
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eprint={2310.15569},
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## Dataset Card Authors
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* Timo Pierre Schrader (Bosch Center for AI, University of Augsburg)
|
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* Matteo Finco (Bosch Research)
|
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+
* Stefan Grünewald (Bosch Center for AI, University of Stuttgart)
|
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* Felix Hildebrand (Bosch Research)
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* Annemarie Friedrich (University of Augsburg)
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## Dataset Card Contact
|
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|
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For all questions, please contact [Timo Schrader](mailto:[email protected]).
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