DOI: 10.5281/zenodo.7525182
title: Argument Aspect Corpus

description: The Argument Aspect Corpus (AAC) contains argumentative English-language sentences from four different topics with aspect annotations on a token level. It was introduced in this paper: Mattes Ruckdeschel and Gregor Wiedemann. 2022. Boundary Detection and Categorization of Argument Aspects via Supervised Learning. In Proceedings of the 9th Workshop on Argument Mining, pages 126–136, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics. The Corpus is based on the argumentative sentences in the UKP SAM[1] dataset for four highly debated topics: nuclear energy, minimum wage, abortion, and marijuana legalization. The corpus contains one conll-formatted file per topic, containing the gold standard annotation. Further the coding guidelines used for annotation are uploaded. of all sentences from that topic. For the reproduction of paper results, check out the corresponding GitHub repository. The gold standard annotation was obtained by <em>chunk-normalization</em> of a token-level gold standard. Using the default chunker from flair[2], sentences were split into chunks, and all tokens of a chunk were labeled with an aspect if at least one token in the chunk was labeled. Any conflicts were resolved by an additional coder. Coding was done by two trained expert coders with a background in Social science. Conflicts were resolved by a third trained coder with a background in Computer Science. <strong>Topic information</strong> The following tables shows statistics for the different topics. \(\alpha_k\) gives the intercoder-agreement as Krippendorff’s alpha. <em>Arg Occurrences</em> gives the number of arguments containig a specific aspect, while <em>Chunk Occurrences</em> gives the number chunks that have been labeled with a specific aspect. <strong>General Statistics</strong> \(N_{args}\) describes the number of arguments for a topic and \(N_{singles}\) the amount of arguments with only one aspect. <strong>Topic</strong> \(N_{args}\) \(N_{singles}\) Minimum Wage (MW) 1118 938 Nuclear Energy (NE) 1261 992 Marijuana Legalization (MJ) 1213 1006 Abortion (AB) 1502 1305 <strong>Minimum Wage</strong> <strong>Aspect</strong> \(\alpha_k\) Arg Occurrences Chunk Occurrences Un/employment rate 0.80 259 287 Motivation/chances 0.67 86 107 Competition/business challenges 0.58 104 129 Prices 0.88 93 104 Social justice/injustice 0.70 305 353 Welfare 0.76 49 57 Economic impact 0.80 81 99 Turnover 0.96 22 32 Capital vs labour 0.51 25 32 Government 0.65 38 71 Low-skilled 0.69 85 100 Youth and secondary wage earners 0.58 24 37 Other 0.56 160 160 all topics 0.65 1331 1568 <strong>Nuclear Energy</strong> <strong>Aspect</strong> \(\alpha_k\) Arg Occurrences Chunk Occurrences Waste 0.80 121 152 Health effects 0.67 100 128 Environmental impact 0.58 236 313 Costs 0.88 131 170 Weapons 0.70 60 66 Reliability 0.76 106 134 Technological innovation 0.80 59 79 Energy policy 0.96 99 135 Renewables 0.51 121 143 Fossil fuels 0.65 99 120 Accidents/security 0.69 270 365 Public debate 0.58 47 75 Other 0.56 139 139 all topics 0.65 1585 2017 <strong>Marijuana Legalization</strong> <strong>Aspect</strong> \(\alpha_k\) Arg Occurrences Chunk Occurrences Illegal trade 0.87 100 130 Child and teen safety 0.89 124 149 Community/Societal effects 0.54 153 196 Health/Psychological effects 0.78 188 302 Medical Marijuana 0.92 134 183 Drug abuse 0.78 66 78 Addiction 0.95 59 72 Personal freedom 0.79 41 54 National budget 0.77 114 154 Gateway drug 0.90 47 60 Legal drugs 0.91 108 130 Drug policy 0.50 104 137 Harm 0.53 77 94 Other 0.49 139 139 all topics 0.64 1454 1879 <strong>Abortion</strong> <strong>Aspect</strong> \(\alpha_k\) Arg Occurrencens Chunk Occurrences Bodily autonomy/Women’s rights 0.57 267 385 Fetal/newborn rights 0.83 507 719 Rape 0.96 49 59 Abortion industry 0.84 15 18 Moral/ethical values 0.67 139 173 Safety/health effects of legal abortion 0.81 88 113 Psychological effects of abortion 0.84 60 78 Health effects of pregnancy/childbirth 0.75 95 116 Illegal abortions 0.83 54 75 Responsibility 0.64 59 81 Adoption 0.93 39 44 Consequences of childbirth 0.66 96 130 Fetal defects/disabilities 0.90 47 60 Parental consent 0.80 16 25 Funding of abortions 0.70 20 25 Other 0.48 172 172 all topics 0.66 1723 2273 [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. https://doi.org/10.18653/v1/D18-1402 [2] Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 54–59, Minneapolis, Minnesota. Association for Computational Linguistics.