A comparison of standard spell checking algorithms and a novel binary neural approach

Hodge, Victoria J.; Austin, Jim

doi:10.1109/tkde.2003.1232265

Cited by 50 publications

(33 citation statements)

References 11 publications

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“…The field of document retrieval-and information retrieval in general-can be easily seen to be an instance of max-kernel search: for some given similarity function, return the document that is most similar to the query. Spell checking systems are an interesting corollary of information retrieval and also an instance of maxkernel search [27].…”

Section: Max-kernel Searchmentioning

confidence: 99%

Dual‐tree fast exact max‐kernel search

Curtin

Ram

2014

Statistical Analysis

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The problem of max-kernel search arises everywhere: given a query point pq, a set of reference objects Sr and some kernel K, find arg max pr ∈Sr K(pq, pr). Max-kernel search is ubiquitous and appears in countless domains of science, thanks to the wide applicability of kernels. A few domains include image matching, information retrieval, bio-informatics, similarity search, and collaborative filtering (to name just a few). However, there are no generalized techniques for efficiently solving max-kernel search. This paper presents a single-tree algorithm called single-tree FastMKS which returns the max-kernel solution for a single query point in provably O(log N ) time (where N is the number of reference objects), and also a dual-tree algorithm (dual-tree FastMKS) which is useful for max-kernel search with many query points. If the set of query points is of size O(N ), this algorithm returns a solution in provably O(N ) time, which is significantly better than the O(N 2 ) linear scan solution; these bounds are dependent on the expansion constant of the data. These algorithms work for abstract objects, as they do not require explicit representation of the points in kernel space. Empirical results for a variety of datasets show up to 5 orders of magnitude speedup in some cases. In addition, we present approximate extensions of the FastMKS algorithms that can achieve further speedups. Max-kernel searchOne particularly ubiquitous problem in computer science is that of max-kernel search: for a given set S r of N objects (the reference set), a similarity function K(·, ·), and a query object p q , find the object p r ∈ R such thatOften, max-kernel search is performed for a large set of query objects S q .The most simple approach to this general problem is a linear scan over all the objects in S r . However, the computational cost of this approach scales linearly with the size of the reference set for a single query, making it computationally prohibitive for large datasets. If |S q | = |S r | = O(N ), then this approach scales as O(N 2 ); thus, the approach quickly becomes infeasible for large N .In our setting we restrict the similarity function K(·, ·) to be a Mercer kernel. As we will see, this is not very restrictive. A Mercer kernel is a positive semidefinite kernel function; these can be expressed as an inner product in some Hilbert space H:

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Section: Max-kernel Searchmentioning

confidence: 99%

Dual‐tree fast exact max‐kernel search

Curtin

Ram

2014

Statistical Analysis

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“…Church and Gale (1990) demonstrate the potential of word bigrams to improve the accuracy of isolated word correction Mays et al (1991) used trigram models and obtained 76% accuracy in detection and 73% accuracy in correction. Hodge and Austin (2003) integrate Hamming distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single architecture. Ahmed et al (2010) propose a spell checker that works by selecting the most promising candidates from a ranked list that is derived from n-gram statistics and lexical resources.…”

Section: Correction Candidate Selectionmentioning

confidence: 99%

Selection of correction candidates for the normalization of Spanish user-generated content

et al. 2014

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We present research aiming to build tools for the normalization of User-Generated Content (UGC). We argue that processing this type of text requires the revisiting of the initial steps of Natural Language Processing (NLP), since UGC (micro-blog, blog, and, generally, Web 2.0 user generated texts) presents a number of non-standard communicative and linguistic characteristics -often closer to oral and colloquial language than to edited text. We present a corpus of UGC text in Spanish from three different sources: Twitter, consumer reviews and blogs, and describe its main characteristics. We motivate the need for UGC text normalization by analyzing the problems found when processing this type of text through a conventional language processing pipeline, particularly in the tasks of lemmatization and morphosyntactic tagging.

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“…Many approaches have been applied since people started to deal with this problem. Different techniques like edit distance [4], rule-based techniques [10], n-grams [20], probabilistic techniques [14], neural nets [15], similarity key techniques [16,17] and noisy channel model [18,19] have been proposed. All of these are based on the idea of calculating the similarity between the misspelled word and the words contained in a dictionary.…”

Section: Approaches Of Some Spell Checkersmentioning

confidence: 99%

“…Therefore, it seems reasonable to base correction algorithms on measures that consider these simple operations. However, approaches based on pure n- gram statistics (which account for these operations implicitly) have also proven to provide good performance [1,15].…”

Section: Introductionmentioning

confidence: 99%

Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness

Ahmed¹,

Luca²,

Nürnberger³

2009

Polibits

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Abstract-We present a language-independent spell-checker that is based on an enhancement of the n-gram model. The spell checker is proposing correction suggestions by selecting the most promising candidates from a ranked list of correction candidates that is derived based on n-gram statistics and lexical resources. Besides motivating and describing the developed techniques, we briefly discuss the use of the proposed approach in an application for keyword-and semantic-based search support. In addition, the proposed tool was compared with state-of-the-art spelling correction approaches. The evaluation showed that it outperforms the other methods.

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A comparison of standard spell checking algorithms and a novel binary neural approach

Cited by 50 publications

References 11 publications

Dual‐tree fast exact max‐kernel search

Dual‐tree fast exact max‐kernel search

Selection of correction candidates for the normalization of Spanish user-generated content

Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness

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