Hyperbolic Interaction Model for Hierarchical Multi-Label Classification

Abstract

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available.

Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification

“…For modeling label dependencies, Zhou et al (2020) formulate the hierarchy as a directed graph and introduce hierarchy-aware structure encoders. Cao et al (2020) and Chen et al (2020a) exploit the hyperbolic representation for labels by encoding the taxonomic hierarchy.…”

A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification

“…We test the proposed model not with a synthetic dataset, but on a concrete downstream tasks, such as entity typing. Our work resembles López et al (2019) and Chen et al (2019), though they separately learn embeddings for type labels and text representations in hyperbolic space, whereas we do it in an integrated fashion.…”

Metric Learning for Dynamic Text Classification

“…However, their prototypical network uses the Einstein midpoint rather than the Karcher mean we use in Section 3.3. In (Chen et al, 2019) the authors embed the labels and data separately, then predict hierarchical class membership using an interaction model. Our model directly links embedding distances to model predictions, and thus learns an embedded space that is more amenable to low-resource, dynamic classification tasks.…”