Abstract
Text mining pursues producing valuable information from natural language text. Conditions cannot be neglected because it may easily lead to misinterpretations. There are naive proposals to mine conditions that rely on user-defined patterns, which falls short; there is only one machine-learning proposal, but it requires to provide specific-purpose dictionaries, taxonomies, and heuristics, it works on opinion sentences only, and it was evaluated very shallowly. We present a novel hybrid approach that relies on computational linguistics and deep learning; our experiments prove that it is more effective than current proposals in terms of \(F_1\) score and does not have their drawbacks.
Supported by Opileak.com and the Spanish R&D programme (grants TIN2013-40848-R and TIN2013-40848-R). The computing facilities were provided by the Andalusian Scientific Computing Centre (CICA). We also thank Dr. Francisco Herrera for his hints on statistical analyses and sharing his software with us.
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- 1.
Available at https://github.com/FernanOrtega/HAIS18.
- 2.
Available at https://www.kaggle.com/fogallego/reviews-with-conditions.
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Gallego, F.O., Corchuelo, R. (2018). A Hybrid Approach to Mining Conditions. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_22
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