398 research outputs found
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks
Hyperbolic Deep Neural Networks: A Survey
Recently, there has been a rising surge of momentum for deep representation
learning in hyperbolic spaces due to theirhigh capacity of modeling data like
knowledge graphs or synonym hierarchies, possessing hierarchical structure. We
refer to the model as hyperbolic deep neural network in this paper. Such a
hyperbolic neural architecture potentially leads to drastically compact model
withmuch more physical interpretability than its counterpart in Euclidean
space. To stimulate future research, this paper presents acoherent and
comprehensive review of the literature around the neural components in the
construction of hyperbolic deep neuralnetworks, as well as the generalization
of the leading deep approaches to the Hyperbolic space. It also presents
current applicationsaround various machine learning tasks on several publicly
available datasets, together with insightful observations and identifying
openquestions and promising future directions
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
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
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