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Metric-Type Identification for Multilevel Header Numerical Tables in Scientific Papers
2021
Journal of Natural Language Processing
Numerical tables are widely used to present experimental results in scientific papers. For table understanding, a metric-type is essential to discriminate numbers in the tables. Herein, we introduce a new information extraction task, i.e., metric-type identification from multilevel header numerical tables, and provide a dataset extracted from scientific papers comprising header tables, captions, and metric-types. We propose joint-learning neural classification and generation schemes featuring
doi:10.5715/jnlp.28.1247
fatcat:nh3uebxhgndqrddyjrhuojb3z4