Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks. On the other side, deep neural networks are effective in learning non-linear decision functions. Recent works demonstrated that expressive kernels and deep neural networks can be combined in a Kernel-based Deep Architecture (KDA), a common framework that allows to explicitly model structured information into a neural network. This combination achieves state-of-the-art accuracy in different semantic inference tasks. This paper investigates the impact of linguistic information on the performance reachable by a KDA by studying the benefits that different kernels can bring to the inference quality. We believe that the expressiveness of data representations will play a key role in the wide spread adoption of neural networks in AI problem solving. We experimentally evaluated the adoption of different kernels (each characterized by a growing expressive power) in a Question Classification task. Results suggest the importance of rich kernel functions in optimizing the accuracy of a KDA.
CITATION STYLE
Croce, D., Filice, S., & Basili, R. (2017). On the impact of linguistic information in kernel-based deep architectures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10640 LNAI, pp. 359–371). Springer Verlag. https://doi.org/10.1007/978-3-319-70169-1_27
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