38,704 research outputs found
Detection is the central problem in real-word spelling correction
Real-word spelling correction differs from non-word spelling correction in
its aims and its challenges. Here we show that the central problem in real-word
spelling correction is detection. Methods from non-word spelling correction,
which focus instead on selection among candidate corrections, do not address
detection adequately, because detection is either assumed in advance or heavily
constrained. As we demonstrate in this paper, merely discriminating between the
intended word and a random close variation of it within the context of a
sentence is a task that can be performed with high accuracy using
straightforward models. Trigram models are sufficient in almost all cases. The
difficulty comes when every word in the sentence is a potential error, with a
large set of possible candidate corrections. Despite their strengths, trigram
models cannot reliably find true errors without introducing many more, at least
not when used in the obvious sequential way without added structure. The
detection task exposes weakness not visible in the selection task
Fifty years of spellchecking
A short history of spellchecking from the late 1950s to the present day, describing its development through dictionary lookup, affix stripping, correction, confusion sets, and edit distance to the use of gigantic databases
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
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for
clinical free-text that uses word and character n-gram embeddings. Our method
generates misspelling replacement candidates and ranks them according to their
semantic fit, by calculating a weighted cosine similarity between the
vectorized representation of a candidate and the misspelling context. To tune
the parameters of this model, we generate self-induced spelling error corpora.
We perform our experiments for two languages. For English, we greatly
outperform off-the-shelf spelling correction tools on a manually annotated
MIMIC-III test set, and counter the frequency bias of a noisy channel model,
showing that neural embeddings can be successfully exploited to improve upon
the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling
correction tool on manually annotated clinical records from the Antwerp
University Hospital, but can offer no empirical evidence that our method
counters the frequency bias of a noisy channel model in this case as well.
However, both our context-sensitive model and our implementation of the noisy
channel model obtain high scores on the test set, establishing a
state-of-the-art for Dutch clinical spelling correction with the noisy channel
model.Comment: Appears in volume 7 of the CLIN Journal,
http://www.clinjournal.org/biblio/volum
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