2,356 research outputs found
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
Group Minds and the Case of Wikipedia
Group-level cognitive states are widely observed in human social systems, but
their discussion is often ruled out a priori in quantitative approaches. In
this paper, we show how reference to the irreducible mental states and
psychological dynamics of a group is necessary to make sense of large scale
social phenomena. We introduce the problem of mental boundaries by reference to
a classic problem in the evolution of cooperation. We then provide an explicit
quantitative example drawn from ongoing work on cooperation and conflict among
Wikipedia editors, showing how some, but not all, effects of individual
experience persist in the aggregate. We show the limitations of methodological
individualism, and the substantial benefits that come from being able to refer
to collective intentions, and attributions of cognitive states of the form
"what the group believes" and "what the group values".Comment: 21 pages, 6 figures; matches published versio
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
Spartan Daily, January 26, 1996
Volume 106, Issue 2https://scholarworks.sjsu.edu/spartandaily/8787/thumbnail.jp
Two polymorphisms facilitate differences in plasticity between two chicken major histocompatibility complex class I proteins
Major histocompatibility complex class I molecules (MHC I) present peptides to cytotoxic T-cells at the surface of almost all nucleated cells. The function of MHC I molecules is to select high affinity peptides from a large intracellular pool and they are assisted in this process by co-factor molecules, notably tapasin. In contrast to mammals, MHC homozygous chickens express a single MHC I gene locus, termed BF2, which is hypothesised to have co-evolved with the highly polymorphic tapasin within stable haplotypes. The BF2 molecules of the B15 and B19 haplotypes have recently been shown to differ in their interactions with tapasin and in their peptide selection properties. This study investigated whether these observations might be explained by differences in the protein plasticity that is encoded into the MHC I structure by primary sequence polymorphisms. Furthermore, we aimed to demonstrate the utility of a complimentary modelling approach to the understanding of complex experimental data. Combining mechanistic molecular dynamics simulations and the primary sequence based technique of statistical coupling analysis, we show how two of the eight polymorphisms between BF2*15:01 and BF2*19:01 facilitate differences in plasticity. We show that BF2*15:01 is intrinsically more plastic than BF2*19:01, exploring more conformations in the absence of peptide. We identify a protein sector of contiguous residues connecting the membrane bound ?3 domain and the heavy chain peptide binding site. This sector contains two of the eight polymorphic residues. One is residue 22 in the peptide binding domain and the other 220 is in the ?3 domain, a putative tapasin binding site. These observations are in correspondence with the experimentally observed functional differences of these molecules and suggest a mechanism for how modulation of MHC I plasticity by tapasin catalyses peptide selection allosterically
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