A Winnow-Based Approach to Context-Sensitive Spelling Correction

A large class of machine-learning problems in natural language require the characterization of linguistic context. Two characteristic properties of such problems are that their feature space is of very high dimensionality, and their target concepts refer to only a small subset of the features in the space. Under such conditions, multiplicative weight-update algorithms such as Winnow have been shown to have exceptionally good theoretical properties. We present an algorithm combining variants of Winnow and weighted-majority voting, and apply it to a problem in the aforementioned class: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting "to" for "too", "casual" for "causal", etc. We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a statistics-based method representing the state of the art for this task. We find: (1) When run with a full (unpruned) set of features, WinSpell achieves accuracies significantly higher than BaySpell was able to achieve in either the pruned or unpruned condition; (2) When compared with other systems in the literature, WinSpell exhibits the highest performance; (3) The primary reason that WinSpell outperforms BaySpell is that WinSpell learns a better linear separator; (4) When run on a test set drawn from a different corpus than the training set was drawn from, WinSpell is better able than BaySpell to adapt, using a strategy we will present that combines supervised learning on the training set with unsupervised learning on the (noisy) test set.Comment: To appear in Machine Learning, Special Issue on Natural Language Learning, 1999. 25 page

Applying Winnow to Context-Sensitive Spelling Correction

Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting {\it to\/} for {\it too}, {\it casual\/} for {\it causal}, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classifiers. We find that: (1)~When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2)~When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3)~When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set.Comment: 9 page

A survey of techniques for refrigeration, reliquefaction, and production of slush for hydrogen

Several techniques were surveyed for the refrigeration, reliquefaction and production of slush from hydrogen. The techniques included auger; bubbling helium gas; Simon desorption; the Petlier effect; Joule-Kelvin expansion using Stirling, Brayton, and Viulleumirer approaches; rotary reciprocating; a dilution refrigerator; adiabatic demagnetization of a paramagnetic salt; and adiabatic magnetization of a superconductor

Spatiotemporal dynamics in 2D Kolmogorov flow over large domains

Kolmogorov flow in two dimensions - the two-dimensional Navier-Stokes equations with a sinusoidal body force - is considered over extended periodic domains to reveal localised spatiotemporal complexity. The flow response mimicks the forcing at small forcing amplitudes but beyond a critical value develops a long wavelength instability. The ensuing state is described by a Cahn-Hilliard-type equation and as a result coarsening dynamics are observed for random initial data. After further bifurcations, this regime gives way to multiple attractors, some of which possess spatially-localised time dependence. Co-existence of such attractors in a large domain gives rise to interesting collisional dynamics which is captured by a system of 5 (1-space and 1-time) PDEs based on a long wavelength limit. The coarsening regime reinstates itself at yet higher forcing amplitudes in the sense that only longest-wavelength solutions remain attractors. Eventually, there is one global longest-wavelength attractor which possesses two localised chaotic regions - a kink and antikink - which connect two steady one-dimensional flow regions of essentially half the domain width each. The wealth of spatiotemporal complexity uncovered presents a bountiful arena in which to study the existence of simple invariant localised solutions which presumably underpin all of the observed behaviour

Two Emission Mechanisms in the Fermi Bubbles: A Possible Signal of Annihilating Dark Matter

We study the variation of the spectrum of the Fermi Bubbles with Galactic latitude. Far from the Galactic plane (|b| > 30 degrees), the observed gamma-ray emission is nearly invariant with latitude, and is consistent with arising from inverse Compton scattering of the interstellar radiation field by cosmic-ray electrons with an approximately power-law spectrum. The same electrons in the presence of microgauss-scale magnetic fields can also generate the the observed microwave "haze". At lower latitudes (b < 20 degrees), in contrast, the spectrum of the emission correlated with the Bubbles possesses a pronounced spectral feature peaking at 1-4 GeV (in E^2 dN/dE) which cannot be generated by any realistic spectrum of electrons. Instead, we conclude that a second (non-inverse-Compton) emission mechanism must be responsible for the bulk of the low-energy, low-latitude emission. This second component is spectrally similar to the excess GeV emission previously reported from the Galactic Center (GC), and also appears spatially consistent with a luminosity per volume falling approximately as r^-2.4, where r is the distance from the GC. We argue that the spectral feature visible in the low-latitude Bubbles is the extended counterpart of the GC excess, now detected out to at least 2-3 kpc from the GC. The spectrum and angular distribution of the signal is consistent with that predicted from ~10 GeV dark matter particles annihilating to leptons, or from ~50 GeV dark matter particles annihilating to quarks, following a distribution similar to the canonical Navarro-Frenk-White (NFW) profile. We also consider millisecond pulsars as a possible astrophysical explanation for the signal, as observed millisecond pulsars possess a spectral cutoff at approximately the required energy. Any such scenario would require a large population of unresolved millisecond pulsars extending at least 2-3 kpc from the GC.Comment: 26 pages, 20 figure