4,876 research outputs found
Do not forget: Full memory in memory-based learning of word pronunciation
Memory-based learning, keeping full memory of learning material, appears a
viable approach to learning NLP tasks, and is often superior in generalisation
accuracy to eager learning approaches that abstract from learning material.
Here we investigate three partial memory-based learning approaches which remove
from memory specific task instance types estimated to be exceptional. The three
approaches each implement one heuristic function for estimating exceptionality
of instance types: (i) typicality, (ii) class prediction strength, and (iii)
friendly-neighbourhood size. Experiments are performed with the memory-based
learning algorithm IB1-IG trained on English word pronunciation. We find that
removing instance types with low prediction strength (ii) is the only tested
method which does not seriously harm generalisation accuracy. We conclude that
keeping full memory of types rather than tokens, and excluding minority
ambiguities appear to be the only performance-preserving optimisations of
memory-based learning.Comment: uses conll98, epsf, and ipamacs (WSU IPA
Morphological Analysis as Classification: an Inductive-Learning Approach
Morphological analysis is an important subtask in text-to-speech conversion,
hyphenation, and other language engineering tasks. The traditional approach to
performing morphological analysis is to combine a morpheme lexicon, sets of
(linguistic) rules, and heuristics to find a most probable analysis. In
contrast we present an inductive learning approach in which morphological
analysis is reformulated as a segmentation task. We report on a number of
experiments in which five inductive learning algorithms are applied to three
variations of the task of morphological analysis. Results show (i) that the
generalisation performance of the algorithms is good, and (ii) that the lazy
learning algorithm IB1-IG performs best on all three tasks. We conclude that
lazy learning of morphological analysis as a classification task is indeed a
viable approach; moreover, it has the strong advantages over the traditional
approach of avoiding the knowledge-acquisition bottleneck, being fast and
deterministic in learning and processing, and being language-independent.Comment: 11 pages, 5 encapsulated postscript figures, uses non-standard NeMLaP
proceedings style nemlap.sty; inputs ipamacs (international phonetic
alphabet) and epsf macro
Electrodynamics from Noncommutative Geometry
Within the framework of Connes' noncommutative geometry, the notion of an
almost commutative manifold can be used to describe field theories on compact
Riemannian spin manifolds. The most notable example is the derivation of the
Standard Model of high energy physics from a suitably chosen almost commutative
manifold. In contrast to such a non-abelian gauge theory, it has long been
thought impossible to describe an abelian gauge theory within this framework.
The purpose of this paper is to improve on this point. We provide a simple
example of a commutative spectral triple based on the two-point space, and show
that it yields a U(1) gauge theory. Then, we slightly modify the spectral
triple such that we obtain the full classical theory of electrodynamics on a
curved background manifold.Comment: 16 page
Supersymmetric QCD and noncommutative geometry
We derive supersymmetric quantum chromodynamics from a noncommutative
manifold, using the spectral action principle of Chamseddine and Connes. After
a review of the Einstein-Yang-Mills system in noncommutative geometry, we
establish in full detail that it possesses supersymmetry. This noncommutative
model is then extended to give a theory of quarks, squarks, gluons and gluinos
by constructing a suitable noncommutative spin manifold (i.e. a spectral
triple). The particles are found at their natural place in a spectral triple:
the quarks and gluinos as fermions in the Hilbert space, the gluons and squarks
as bosons as the inner fluctuations of a (generalized) Dirac operator by the
algebra of matrix-valued functions on a manifold. The spectral action principle
applied to this spectral triple gives the Lagrangian of supersymmetric QCD,
including soft supersymmetry breaking mass terms for the squarks. We find that
these results are in good agreement with the physics literature
Forgetting Exceptions is Harmful in Language Learning
We show that in language learning, contrary to received wisdom, keeping
exceptional training instances in memory can be beneficial for generalization
accuracy. We investigate this phenomenon empirically on a selection of
benchmark natural language processing tasks: grapheme-to-phoneme conversion,
part-of-speech tagging, prepositional-phrase attachment, and base noun phrase
chunking. In a first series of experiments we combine memory-based learning
with training set editing techniques, in which instances are edited based on
their typicality and class prediction strength. Results show that editing
exceptional instances (with low typicality or low class prediction strength)
tends to harm generalization accuracy. In a second series of experiments we
compare memory-based learning and decision-tree learning methods on the same
selection of tasks, and find that decision-tree learning often performs worse
than memory-based learning. Moreover, the decrease in performance can be linked
to the degree of abstraction from exceptions (i.e., pruning or eagerness). We
provide explanations for both results in terms of the properties of the natural
language processing tasks and the learning algorithms.Comment: 31 pages, 7 figures, 10 tables. uses 11pt, fullname, a4wide tex
styles. Pre-print version of article to appear in Machine Learning 11:1-3,
Special Issue on Natural Language Learning. Figures on page 22 slightly
compressed to avoid page overloa
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