Jurkschat, Lena
Wiedemann, Gregor
Heinrich, Maximilian
Ruckdeschel, Mattes
Torge, Sunna
2022-04-19
<p>The Argument Aspect Corpus–Nuclear Energy (AAC-NE) contains English-language sentences with aspect annotations describing the content of arguments on the topic of nuclear energy.</p>
<p>It was introduced in this paper:</p>
<blockquote>
<p>Jurkschat, L., Wiedemann, G., Heinrich, M., Ruckdeschel, M., & Torge, S. (2022). Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association (ELRA).</p>
</blockquote>
<p>The AAC-NE corpus is based on a subset of all argumentative sentences contained in the UKP SAM dataset [1] for which a majority vote of three annotators could be achieved during the annotation of the main argument aspect of each sentence.</p>
<p>The CSV files contain one of nine aspect labels per argumentative sentence split into training, dev, and test set.</p>
<table>
<thead>
<tr>
<th><strong>aspect</strong></th>
<th><strong>train</strong></th>
<th><strong>dev</strong></th>
<th><strong>test</strong></th>
<th><strong>Sum</strong></th>
<th><strong>Kripp. Alpha</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td>alternatives</td>
<td>100</td>
<td>16</td>
<td>21</td>
<td>137</td>
<td>0.69</td>
</tr>
<tr>
<td>costs</td>
<td>98</td>
<td>17</td>
<td>29</td>
<td>144</td>
<td>0.72</td>
</tr>
<tr>
<td>environment</td>
<td>209</td>
<td>27</td>
<td>64</td>
<td>300</td>
<td>0.74</td>
</tr>
<tr>
<td>innovation</td>
<td>33</td>
<td>2</td>
<td>8</td>
<td>43</td>
<td>0.38</td>
</tr>
<tr>
<td>reactor safety</td>
<td>112</td>
<td>17</td>
<td>43</td>
<td>172</td>
<td>0.59</td>
</tr>
<tr>
<td>reliability</td>
<td>47</td>
<td>5</td>
<td>10</td>
<td>62</td>
<td>0.36</td>
</tr>
<tr>
<td>waste</td>
<td>87</td>
<td>5</td>
<td>26</td>
<td>118</td>
<td>0.80</td>
</tr>
<tr>
<td>weapons</td>
<td>52</td>
<td>11</td>
<td>15</td>
<td>78</td>
<td>0.77</td>
</tr>
<tr>
<td>other</td>
<td>120</td>
<td>23</td>
<td>29</td>
<td>172</td>
<td>0.49</td>
</tr>
<tr>
<td><strong>all</strong></td>
<td><strong>858</strong></td>
<td><strong>123</strong></td>
<td><strong>245</strong></td>
<td><strong>1226</strong></td>
<td><strong>0.62</strong></td>
</tr>
<tr>
<td>pro</td>
<td> </td>
<td> </td>
<td> </td>
<td>706</td>
<td> </td>
</tr>
<tr>
<td>cons</td>
<td> </td>
<td> </td>
<td> </td>
<td>520</td>
<td> </td>
</tr>
</tbody>
</table>
<p>Additionally, it contains 2000 unlabeled sentences with presumably argumentative content sampled from the newspaper “The Guardian”.</p>
<p>[1] Stab, C., Miller, T., Schiller, B., Rai, P., & Gurevych, I. Cross-topic Argument Mining from Heterogeneous Sources. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3664–3674). Association for Computational Linguistics. <a href="https://doi.org/10.18653/v1/D18-1402">https://doi.org/10.18653/v1/D18-1402 </a></p>
<p> </p>
https://doi.org/10.5281/zenodo.6470232
oai:zenodo.org:6470232
eng
Zenodo
https://doi.org/10.5281/zenodo.6470231
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
LREC, Language Resources and Evaluation, Marseille, France, 2022
Natural Language Processing
Argument Mining
Argument Aspect
Argument Aspect Corpus - Nuclear Energy
info:eu-repo/semantics/other