Tree Edit Distance Learning via Adaptive Symbol Embeddings: Supplementary Materials and Results

TL;DR

This paper introduces a novel metric learning method for trees that learns node embeddings to improve classification, outperforming existing approaches across diverse datasets including biomedical and natural language data.

Contribution

It proposes a new approach to learn tree edit distances indirectly through node embeddings, ensuring metric properties and better interpretability.

Findings

Outperforms state-of-the-art on six benchmark datasets

Effective across diverse domains including computer science and biomedical data

Scales to large datasets with over 300,000 nodes

Abstract

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 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 our proposed metric learning approach improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.