25 research outputs found
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
Learning Irreducible Representations of Noncommutative Lie Groups
Recent work has constructed neural networks that are equivariant to
continuous symmetry groups such as 2D and 3D rotations. This is accomplished
using explicit group representations to derive the equivariant kernels and
nonlinearities. We present two contributions motivated by frontier applications
of equivariance beyond rotations and translations. First, we relax the
requirement for explicit Lie group representations, presenting a novel
algorithm that finds irreducible representations of noncommutative Lie groups
given only the structure constants of the associated Lie algebra. Second, we
demonstrate that Lorentz-equivariance is a useful prior for object-tracking
tasks and construct the first object-tracking model equivariant to the
Poincar\'e group.Comment: 15 pages, 5 figure
Discriminative Topic Mining via Category-Name Guided Text Embedding
Mining a set of meaningful and distinctive topics automatically from massive
text corpora has broad applications. Existing topic models, however, typically
work in a purely unsupervised way, which often generate topics that do not fit
users' particular needs and yield suboptimal performance on downstream tasks.
We propose a new task, discriminative topic mining, which leverages a set of
user-provided category names to mine discriminative topics from text corpora.
This new task not only helps a user understand clearly and distinctively the
topics he/she is most interested in, but also benefits directly keyword-driven
classification tasks. We develop CatE, a novel category-name guided text
embedding method for discriminative topic mining, which effectively leverages
minimal user guidance to learn a discriminative embedding space and discover
category representative terms in an iterative manner. We conduct a
comprehensive set of experiments to show that CatE mines high-quality set of
topics guided by category names only, and benefits a variety of downstream
applications including weakly-supervised classification and lexical entailment
direction identification.Comment: WWW 2020. (Code: https://github.com/yumeng5/CatE
Hyperbolic Hierarchical Contrastive Hashing
Hierarchical semantic structures, naturally existing in real-world datasets,
can assist in capturing the latent distribution of data to learn robust hash
codes for retrieval systems. Although hierarchical semantic structures can be
simply expressed by integrating semantically relevant data into a high-level
taxon with coarser-grained semantics, the construction, embedding, and
exploitation of the structures remain tricky for unsupervised hash learning. To
tackle these problems, we propose a novel unsupervised hashing method named
Hyperbolic Hierarchical Contrastive Hashing (HHCH). We propose to embed
continuous hash codes into hyperbolic space for accurate semantic expression
since embedding hierarchies in hyperbolic space generates less distortion than
in hyper-sphere space and Euclidean space. In addition, we extend the K-Means
algorithm to hyperbolic space and perform the proposed hierarchical hyperbolic
K-Means algorithm to construct hierarchical semantic structures adaptively. To
exploit the hierarchical semantic structures in hyperbolic space, we designed
the hierarchical contrastive learning algorithm, including hierarchical
instance-wise and hierarchical prototype-wise contrastive learning. Extensive
experiments on four benchmark datasets demonstrate that the proposed method
outperforms the state-of-the-art unsupervised hashing methods. Codes will be
released.Comment: 12 pages, 8 figure
Taxonomy completion via implicit concept insertion
High quality taxonomies play a critical role in various domains
such as e-commerce, web search and ontology engineering. While
there has been extensive work on expanding taxonomies from
externally mined data, there has been less attention paid to enriching taxonomies by exploiting existing concepts and structure
within the taxonomy. In this work, we show the usefulness of this
kind of enrichment, and explore its viability with a new taxonomy
completion system ICON (Implicit CONcept Insertion). ICON generates new concepts by identifying implicit concepts based on the
existing concept structure, generating names for such concepts
and inserting them in appropriate positions within the taxonomy.
ICON integrates techniques from entity retrieval, text summary,
and subsumption prediction; this modular architecture offers high
flexibility while achieving state-of-the-art performance. We have
evaluated ICON on two e-commerce taxonomies, and the results
show that it offers significant advantages over strong baselines including recent taxonomy completion models and the large language
model, ChatGPT