1,058 research outputs found
AutoSense Model for Word Sense Induction
Word sense induction (WSI), or the task of automatically discovering multiple
senses or meanings of a word, has three main challenges: domain adaptability,
novel sense detection, and sense granularity flexibility. While current latent
variable models are known to solve the first two challenges, they are not
flexible to different word sense granularities, which differ very much among
words, from aardvark with one sense, to play with over 50 senses. Current
models either require hyperparameter tuning or nonparametric induction of the
number of senses, which we find both to be ineffective. Thus, we aim to
eliminate these requirements and solve the sense granularity problem by
proposing AutoSense, a latent variable model based on two observations: (1)
senses are represented as a distribution over topics, and (2) senses generate
pairings between the target word and its neighboring word. These observations
alleviate the problem by (a) throwing garbage senses and (b) additionally
inducing fine-grained word senses. Results show great improvements over the
state-of-the-art models on popular WSI datasets. We also show that AutoSense is
able to learn the appropriate sense granularity of a word. Finally, we apply
AutoSense to the unsupervised author name disambiguation task where the sense
granularity problem is more evident and show that AutoSense is evidently better
than competing models. We share our data and code here:
https://github.com/rktamplayo/AutoSense.Comment: AAAI 201
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in
functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior
during a scanning session. Such predictions suffer from the huge number of
brain regions sampled on the voxel grid of standard fMRI data sets: the curse
of dimensionality. Dimensionality reduction is thus needed, but it is often
performed using a univariate feature selection procedure, that handles neither
the spatial structure of the images, nor the multivariate nature of the signal.
By introducing a hierarchical clustering of the brain volume that incorporates
connectivity constraints, we reduce the span of the possible spatial
configurations to a single tree of nested regions tailored to the signal. We
then prune the tree in a supervised setting, hence the name supervised
clustering, in order to extract a parcellation (division of the volume) such
that parcel-based signal averages best predict the target information.
Dimensionality reduction is thus achieved by feature agglomeration, and the
constructed features now provide a multi-scale representation of the signal.
Comparisons with reference methods on both simulated and real data show that
our approach yields higher prediction accuracy than standard voxel-based
approaches. Moreover, the method infers an explicit weighting of the regions
involved in the regression or classification task
Calibrated model-based evidential clustering using bootstrapping
Evidential clustering is an approach to clustering in which
cluster-membership uncertainty is represented by a collection of
Dempster-Shafer mass functions forming an evidential partition. In this paper,
we propose to construct these mass functions by bootstrapping finite mixture
models. In the first step, we compute bootstrap percentile confidence intervals
for all pairwise probabilities (the probabilities for any two objects to belong
to the same class). We then construct an evidential partition such that the
pairwise belief and plausibility degrees approximate the bounds of the
confidence intervals. This evidential partition is calibrated, in the sense
that the pairwise belief-plausibility intervals contain the true probabilities
"most of the time", i.e., with a probability close to the defined confidence
level. This frequentist property is verified by simulation, and the practical
applicability of the method is demonstrated using several real datasets
A review of model designs
The PAEQANN project aims to review current ecological theories which can help identify suited models that predict community structure in aquatic ecosystems, to select and discuss appropriate models, depending on the type of target community (i.e. empirical vs. simulation models) and to examine how results add to ecological water management objectives. To reach these goals a number of classical statistical models, artificial neural networks and dynamic models are presented. An even higher number of techniques within these groups will tested lateron in the project. This report introduces all of them. The techniques are shortly introduced, their algorithms explained, and the advantages and disadvantages discussed
Recent Developments in Document Clustering
This report aims to give a brief overview of the current state of document clustering research and present recent developments in a well-organized manner. Clustering algorithms are considered with two hypothetical scenarios in mind: online query clustering with tight efficiency constraints, and offline clustering with an emphasis on accuracy. A comparative analysis of the algorithms is performed along with a table summarizing important properties, and open problems as well as directions for future research are discussed
Generalized topographic block model
Co-clustering leads to parsimony in data visualisation with a number of parameters dramatically reduced in comparison to the dimensions of the data sample. Herein, we propose a new generalized approach for nonlinear mapping by a re-parameterization of the latent block mixture model. The densities modeling the blocks are in an exponential family such that the Gaussian, Bernoulli and Poisson laws are particular cases. The inference of the parameters is derived from the block expectation–maximization algorithm with a Newton–Raphson procedure at the maximization step. Empirical experiments with textual data validate the interest of our generalized model
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