Abstract
Causal relation extraction is the task of identifying and extracting the causal relations occurring in a text. We present an approach that is especially suitable for extracting relations between complex events – which are assumed to be already identified – as found in natural science literature, supporting literature-based knowledge discovery. The approach is based on supervised learning, exploiting a wide range of linguistic features. Experimental results indicate that even with a limited amount of training data, reasonable accuracy can be obtained by using a pipeline of classifiers, optimising hyper-parameters, down-weighting negative instances and applying feature selection methods.
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Notes
- 1.
Originally only so-called quantitative variables were annotated. This constraint has since been dropped.
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Barik, B., Marsi, E., Öztürk, P. (2017). Extracting Causal Relations Among Complex Events in Natural Science Literature. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_13
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DOI: https://doi.org/10.1007/978-3-319-59569-6_13
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