Topic
String-to-string correction problem
About: String-to-string correction problem is a research topic. Over the lifetime, 426 publications have been published within this topic receiving 22312 citations.
Papers published on a yearly basis
Papers
TL;DR: An algorithm is presented which solves the string-to-string correction problem in time proportional to the product of the lengths of the two strings.
Abstract: The string-to-string correction problem is to determine the distance between two strings as measured by the minimum cost sequence of “edit operations” needed to change the one string into the other. The edit operations investigated allow changing one symbol of a string into another single symbol, deleting one symbol from a string, or inserting a single symbol into a string. An algorithm is presented which solves this problem in time proportional to the product of the lengths of the two strings. Possible applications are to the problems of automatic spelling correction and determining the longest subsequence of characters common to two strings.
3,523 citations
TL;DR: This work surveys the current techniques to cope with the problem of string matching that allows errors, and focuses on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms.
Abstract: We survey the current techniques to cope with the problem of string matching that allows errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices. We conclude with some directions for future work and open problems.
3,075 citations
Book•
1 Aug 1983
TL;DR: In this paper, a mudflap assembly for use with a dump vehicle having dual tires at the rear end thereof and including a pair of flexible flap sections one of which is supported by a rigid member adjacent the dual tires and the other is located above and to the rear of the rigid member and is secured at its upper end to the dump body.
Abstract: A mudflap assembly for use with a dump vehicle having dual tires at the rear end thereof and including a pair of flexible flap sections one of which is supported by a rigid member adjacent the dual tires and the other is located above and to the rear of the rigid member and is secured at its upper end to the dump body. The rigid member is pivotally connected to the dump body and is combined with a cable which assures that the attached flap section maintains substantially the same position when the dump body is in the lowered-carry-position or raised-dump-position.
1,965 citations
TL;DR: The stochastic model allows us to learn a string-edit distance function from a corpus of examples and is applicable to any string classification problem that may be solved using a similarity function against a database of labeled prototypes.
Abstract: In many applications, it is necessary to determine the similarity of two strings. A widely-used notion of string similarity is the edit distance: the minimum number of insertions, deletions, and substitutions required to transform one string into the other. In this report, we provide a stochastic model for string-edit distance. Our stochastic model allows us to learn a string-edit distance function from a corpus of examples. We illustrate the utility of our approach by applying it to the difficult problem of learning the pronunciation of words in conversational speech. In this application, we learn a string-edit distance with nearly one-fifth the error rate of the untrained Levenshtein distance. Our approach is applicable to any string classification problem that may be solved using a similarity function against a database of labeled prototypes.
994 citations
TL;DR: This work surveys the problem of comparing labeled trees based on simple local operations of deleting, inserting, and relabeling nodes and presents one or more of the central algorithms for solving the problem.
920 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2018 | 1 |
| 2017 | 6 |
| 2016 | 13 |
| 2015 | 23 |
| 2014 | 18 |
| 2013 | 13 |