J. Kim and J. Shawe-Taylor. Fast string matching using an n-gram algorithm. University of London, 1991. Home/Search Document Details and Download
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Text Searching: Theory and Practice - Baeza-Yates, Navarro (Correct)
....i i d[t i m ] Figure 6: Horspool algorithm with skip loop included. windows by more than 4 positions on average, no matter how large is m. One way to circumvent this weakness is to arti cially enlarge the alphabet. This folklore idea has been reinvented several times, see for example [7, 40, 64]. Say that, instead of considering just the last window character to determine the shift, we read the last q characters (that is, the last window q gram) We preprocess P so that, for each possible q gram, we record its smallest distance to the end of the pattern. Then, we use Horspool as usual. ....
J. Kim and J. Shawe-Taylor. Fast string matching using an n-gram algorithm. University of London, 1991.
Fast String Correction with Levenshtein-Automata - Schulz, Mihov (2002) (Correct)
....appropriate statistical data can be used for re nement of ranking. Similarity between two words can be measured in several ways. Most useful are (dis)similarity measures based on variants of the Levenshteindistance [Lev66, WF74, WBR95, SKS96, OL97] or on n gram distances [AFW83, Ukk92, KST92, KST94] In this paper, we take the Levenshtein distance as a basis. The standard algorithm for computing the Levenshtein distance between two words by Wagner and Fisher [WF74] uses a dynamic programming scheme that leads to quadratic time complexity. Even with more sophisticated algorithms (cf. ....
J.Y. Kim and J. Shawe-Taylor. Fast string matching using an n-gram algorithm. Software{Practice and Experience, 94(1):79-88, 1994.
Fast String Correction with Levenshtein-Automata - Schulz, Mihov (2002) (Correct)
....the word is in the dictionary. In the negative case, the words of the dictionary that are most similar to W are good suggestions for correction. Similarity between two words can be measured in several ways. Most popular are (dis)similarity measures based on n gram distances [AFW83, Ukk92, KST92, KST94] or on variants of the Levenshtein distance [Lev66, WF74, WBR95, SKS96, OL97] In this paper, we take the Levenshtein distance as a basis. 1 The standard algorithm for computing the Levenshtein distance between two words by Wagner and Fisher [WF74] uses a dynamic programming scheme that leads ....
J.Y. Kim and J. Shawe-Taylor. Fast string matching using an n-gram algorithm. Software{Practice and Experience, 94(1):79-88, 1994.
Text Retrieval: Theory and Practice - Baeza-Yates (1992) (3 citations) (Correct)
.... to the Boyer Moore algorithm, through alphabet transformations [BY89b, BY89a] sensitivity to the distribution of the text [BY89b, BY89c, Sun90] word theory [CP91] adaptivity [Smi91] and a taxonomy of string searching algorithms [HS91] ffl fast algorithms for long patterns based on n grams [KST91]; ffl several new algorithms for string matching with mismatches, based on the Boyer Moore approach, which are faster and more practical [BY89b, BYG92, GL89, TU90] ffl algorithms based on partitioning the pattern [WM91, BYP91] ffl faster practical algorithms for string matching with errors, ....
J.Y. Kim and J. Shawe-Taylor. Fast string matching using an n-gram algorithm. University of London, 1991.
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