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EnglishWe introduce an approach to integrate bi-directional contexts in a generative tree model by means of structured transductions. We show how this can be efficiently realized as the composition of a top-down and a bottom-up generative model for trees, that are trained independently within a circular encoding-decoding scheme. The resulting input-driven generative model is shown to capture information concerning bi-directional contexts within its state-space. An experimental evaluation using the Jaccard generative kernel for trees is presented, indicating that the approach can achieve state of the art performance on tree classification benchmarks.
| Titolo: | Modeling Bi-directional Tree Contexts by Generative Transductions | |
| Autori interni: | ||
| Anno del prodotto: | 2014 | |
| Rivista: | ||
| Abstract: | We introduce an approach to integrate bi-directional contexts in a generative tree model by means of structured transductions. We show how this can be efficiently realized as the composition of a top-down and a bottom-up generative model for trees, that are trained independently within a circular encoding-decoding scheme. The resulting input-driven generative model is shown to capture information concerning bi-directional contexts within its state-space. An experimental evaluation using the Jaccard generative kernel for trees is presented, indicating that the approach can achieve state of the art performance on tree classification benchmarks. | |
| Handle: | http://hdl.handle.net/11568/665864 | |
| ISBN: | 9783319126364 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11568/665864
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