Argument Aspect Corpus
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资源简介：
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 chunk-normalization 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.
Topic information
The following tables shows statistics for the different topics. \(\alpha_k\) gives the intercoder-agreement as Krippendorff’s alpha. Arg Occurrences gives the number of arguments containig a specific aspect, while Chunk Occurrences gives the number chunks that have been labeled with a specific aspect.
General Statistics
\(N_{args}\) describes the number of arguments for a topic and \(N_{singles}\) the amount of arguments with only one aspect.
Topic
\(N_{args}\)
\(N_{singles}\)
Minimum Wage (MW)
1118
938
Nuclear Energy (NE)
1261
992
Marijuana Legalization (MJ)
1213
1006
Abortion (AB)
1502
1305
Minimum Wage
Aspect
\(\alpha_k\)
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
Nuclear Energy
Aspect
\(\alpha_k\)
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
Marijuana Legalization
Aspect
\(\alpha_k\)
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
Abortion
Aspect
\(\alpha_k\)
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.
创建时间：
2023-01-11
