Automatic spelling correction in scientific and scholarly text

Pollock, Joseph J.; Zamora, Antonio

doi:10.1145/358027.358048

Cited by 133 publications

(72 citation statements)

References 12 publications

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“…Noteworthy among the error-correction systems based on nonphonetic coding is SPEEDCOP (Spelling Error Detection-Correction Project) (Pollock, 1982;Pollock & Zamora, 1984), currently used in the databases of Chemical Abstracts Services. The nucleus of this system is an algorithm designed to correct misspellings that consist of a single error, and a dictionary where the correct forms are stored.…”

Section: Similarity Relationsmentioning

confidence: 99%

“…Its method of nonphonetic coding captures the essence of the correct words as well as that of the misspellings, and a word is corrected when the compared codes collate very closely. The codes designated as keys can be of two types: "Skeleton keys" and "Omission keys" (Pollock & Zamora, 1984). The SPEEDCOP correction strategy is based on the creation of a key of similarity for each word of the dictionary and its classification in alphabetic order.…”

Section: Similarity Relationsmentioning

confidence: 99%

“…Many studies focus on automatic spelling correction (Accomazzi, Eichhorn, Kurtz, Grant, & Murray, 2000;Angell, Freund, & Willett, 1983;Blair, 1960;Cucerzan & Brill, 2004;Damerau & Mays, 1989;Davidson, 1962;Gadd, 1988;Kukich, 1992;Landau & Vishkin, 1986;Petersen, 1986;Pollock & Zamora, 1984;Rogers & Willett, 1991;Takahashi, Itoh, Amano, & Yamashita, 1990;Ullmann, 1977;Zobel & Dart, 1995). A number of studies have highlighted the context for correcting such errors and eliminating ambiguity (Damerau, 1964;Hull, 1992;Mays, Damerau, & Mercer, 1991;Riseman & Ehrich, 1971;Schulz & Mihov, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Approximate personal name‐matching through finite‐state graphs

Gálvez

Anegón

2007

J. Am. Soc. Inf. Sci.

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This article shows how finite-state methods can be employed in a new and different task: the conflation of personal name variants in standard forms. In bibliographic databases and citation index systems, variant forms create problems of inaccuracy that affect information retrieval, the quality of information from databases, and the citation statistics used for the evaluation of scientists' work. A number of approximate string matching techniques have been developed to validate variant forms, based on similarity and equivalence relations. We classify the personal name variants as nonvalid and valid forms. In establishing an equivalence relation between valid variants and the standard form of its equivalence class, we defend the application of finite-state transducers. The process of variant identification requires the elaboration of: (a) binary matrices and (b) finite-state graphs. This procedure was tested on samples of author names from bibliographic records, selected from the Library and Information Science Abstracts and Science Citation Index Expanded databases. The evaluation involved calculating the measures of precision and recall, based on completeness and accuracy. The results demonstrate the usefulness of this approach, although it should be complemented with methods based on similarity relations for the recognition of spelling variants and misspellings.

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Section: Similarity Relationsmentioning

confidence: 99%

Section: Similarity Relationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Approximate personal name‐matching through finite‐state graphs

Gálvez

Anegón

2007

J. Am. Soc. Inf. Sci.

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“…Other strategies that might be considered by a server implementor are matches based on substring, suffix, regular expressions, soundex [KNUTH73], and Levenshtein [PZ85] algorithms. These last two are especially useful for correcting spelling errors.…”

Section: The Match Commandmentioning

confidence: 99%

“…"), then the word will be matched using a server-dependent default strategy, which should be the best strategy available for interactive spell checking. This is usually a derivative of the Levenshtein algorithm [PZ85].…”

Section: The Match Commandmentioning

confidence: 99%

A Dictionary Server Protocol

Faith¹,

Martin²

1997

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An evaluation of retrieval effectiveness using spelling-correction and string-similarity matching methods on Malay texts

Bakar

Sembok

Yusoff

2000

J. Am. Soc. Inf. Sci.

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This article evaluates the effectiveness of spelling‐correction and string‐similarity matching methods in retrieving similar words in a Malay dictionary associated with a set of query words. The spelling‐correction techniques used are SPEEDCOP, Soundex, Davidson, Phonix, and Hartlib. Two dynamic‐programming methods that measure longest common subsequence and editcost‐distance are used. Several search combinations of query and dictionary words are performed in the experiments, the best being one that stems both query and dictionary words using an existing Malay stemming algorithm. The retrieval effectiveness (E) and retrieved and relevant (R&R) mean measures are calculated from weighted combination of recall and precision values. Results from these experiments are then compared with available digram, a string‐similarity method. The best R&R and E results are given by using digram. Editcost‐distances produce the best E results, and both dynamic‐programming methods rank second in finding R&R mean measures.

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Automatic spelling correction in scientific and scholarly text

Cited by 133 publications

References 12 publications

Approximate personal name‐matching through finite‐state graphs

Approximate personal name‐matching through finite‐state graphs

A Dictionary Server Protocol

An evaluation of retrieval effectiveness using spelling-correction and string-similarity matching methods on Malay texts

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