59,475 research outputs found
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic
Language modeling for an inflected language
such as Arabic poses new challenges for speech recognition and
machine translation due to its rich morphology. Rich morphology
results in large increases in out-of-vocabulary (OOV) rate and
poor language model parameter estimation in the absence of large
quantities of data. In this study, we present a joint
morphological-lexical language model (JMLLM) that takes
advantage of Arabic morphology. JMLLM combines
morphological segments with the underlying lexical items and
additional available information sources with regards to
morphological segments and lexical items in a single joint model.
Joint representation and modeling of morphological and lexical
items reduces the OOV rate and provides smooth probability
estimates while keeping the predictive power of whole words.
Speech recognition and machine translation experiments in
dialectal-Arabic show improvements over word and morpheme
based trigram language models. We also show that as the
tightness of integration between different information sources
increases, both speech recognition and machine translation
performances improve
Automatic quantitative morphological analysis of interacting galaxies
The large number of galaxies imaged by digital sky surveys reinforces the
need for computational methods for analyzing galaxy morphology. While the
morphology of most galaxies can be associated with a stage on the Hubble
sequence, morphology of galaxy mergers is far more complex due to the
combination of two or more galaxies with different morphologies and the
interaction between them. Here we propose a computational method based on
unsupervised machine learning that can quantitatively analyze morphologies of
galaxy mergers and associate galaxies by their morphology. The method works by
first generating multiple synthetic galaxy models for each galaxy merger, and
then extracting a large set of numerical image content descriptors for each
galaxy model. These numbers are weighted using Fisher discriminant scores, and
then the similarities between the galaxy mergers are deduced using a variation
of Weighted Nearest Neighbor analysis such that the Fisher scores are used as
weights. The similarities between the galaxy mergers are visualized using
phylogenies to provide a graph that reflects the morphological similarities
between the different galaxy mergers, and thus quantitatively profile the
morphology of galaxy mergers.Comment: Astronomy & Computing, accepte
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