24,823 research outputs found
HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
We present our system for semantic frame induction that showed the best
performance in Subtask B.1 and finished as the runner-up in Subtask A of the
SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et
al., 2019). Our approach separates this task into two independent steps: verb
clustering using word and their context embeddings and role labeling by
combining these embeddings with syntactical features. A simple combination of
these steps shows very competitive results and can be extended to process other
datasets and languages.Comment: 5 pages, 3 tables, accepted at SemEval 201
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
A survey of kernel and spectral methods for clustering
Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods. The aim of this paper is to present a survey of kernel and spectral clustering methods, two approaches able to produce nonlinear separating hypersurfaces between clusters. The presented kernel clustering methods are the kernel version of many classical clustering algorithms, e.g., K-means, SOM and neural gas. Spectral clustering arise from concepts in spectral graph theory and the clustering problem is configured as a graph cut problem where an appropriate objective function has to be optimized. An explicit proof of the fact that these two paradigms have the same objective is reported since it has been proven that these two seemingly different approaches have the same mathematical foundation. Besides, fuzzy kernel clustering methods are presented as extensions of kernel K-means clustering algorithm. (C) 2007 Pattem Recognition Society. Published by Elsevier Ltd. All rights reserved
Optimal Clustering under Uncertainty
Classical clustering algorithms typically either lack an underlying
probability framework to make them predictive or focus on parameter estimation
rather than defining and minimizing a notion of error. Recent work addresses
these issues by developing a probabilistic framework based on the theory of
random labeled point processes and characterizing a Bayes clusterer that
minimizes the number of misclustered points. The Bayes clusterer is analogous
to the Bayes classifier. Whereas determining a Bayes classifier requires full
knowledge of the feature-label distribution, deriving a Bayes clusterer
requires full knowledge of the point process. When uncertain of the point
process, one would like to find a robust clusterer that is optimal over the
uncertainty, just as one may find optimal robust classifiers with uncertain
feature-label distributions. Herein, we derive an optimal robust clusterer by
first finding an effective random point process that incorporates all
randomness within its own probabilistic structure and from which a Bayes
clusterer can be derived that provides an optimal robust clusterer relative to
the uncertainty. This is analogous to the use of effective class-conditional
distributions in robust classification. After evaluating the performance of
robust clusterers in synthetic mixtures of Gaussians models, we apply the
framework to granular imaging, where we make use of the asymptotic
granulometric moment theory for granular images to relate robust clustering
theory to the application.Comment: 19 pages, 5 eps figures, 1 tabl
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