Abstract
In this paper, we propose a joint model, composed of neural and linguistic sub-models, to address classification tasks in which the distribution of labels over samples is imbalanced. Different experiments are performed on tasks 1 and 2 of the DEFT 2013 shared task [10]. In one set of experiments, the joint model is used for both classification tasks, whereas the second set of experiments involves using the neural sub-model, independently. This allows us to measure the impact of using linguistic features in the joint model. The results for both tasks show that adding the linguistic sub-model improves classification performance on the rare classes. This improvement is more significant in the case of task 1, where state-of-the-art results are achieved in terms of micro and macro-average F1 scores.
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Mohammadi, E. et al. (2020). Classification of Rare Recipes Requires Linguistic Features as Special Ingredients. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_44
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