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Tree Edit Distance Learning via Adaptive Symbol Embeddings by Paaßen, Benjamin; Gallicchio, Claudio; Micheli, Alessio; Hammer, Barbara is a scholarly article available to read on EtoBox.

What is Tree Edit Distance Learning via Adaptive Symbol Embeddings about?

Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which we call embedding edit distance learning (BEDL) and which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that BEDL

Author

Paaßen, Benjamin; Gallicchio, Claudio; Micheli, Alessio; Hammer, Barbara

Published

2018

Language

EN