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 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.Comment: Paper at the International Conference of Machine Learning (2018),
2018-07-10 to 2018-07-15 in Stockholm, Swede