A technique for computer detection and correction of spelling errors.

Commun. ACM 01/1964; 7:171-176. DOI: 10.1145/363958.363994
Source: DBLP

ABSTRACT The method described assumes that a word which cannot be found in a dictionary has at most one error, which might be a wrong, missing or extra letter or a single transposition. The unidentified input word is compared to the dictionary again, testing each time to see if the words match—assuming one of these errors occurred. During a test run on garbled text, correct identifications were made for over 95 percent of these error types.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Recent work on Textual Entailment has shown a crucial role of knowledge to support entail-ment inferences. However, it has also been demonstrated that currently available entail-ment rules are still far from being optimal. We propose a methodology for the automatic ac-quisition of large scale context-rich entailment rules from Wikipedia revisions, taking advan-tage of the syntactic structure of entailment pairs to define the more appropriate linguis-tic constraints for the rule to be successfully applicable. We report on rule acquisition ex-periments on Wikipedia, showing that it en-ables the creation of an innovative (i.e. ac-quired rules are not present in other available resources) and good quality rule repository.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we visit the problem of approximate string matching allowing for translocations. We study the graph theoretic model proposed by [5] and extending the model, devise an efficient algorithm to solve the approximate string matching allowing for translocations. The resulting algorithm is an adaptation of the classic shift-and algorithm. For patterns having length similar to the word-size of the target machine, the algorithm runs in O(n + mk 2) time for fixed length translocation where n, m and k are the length of the text, pattern and the translocation respectively.
    International Workshop On Combinatorial Algorithms; 07/2012
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Being an agglutinative language Kazakh imposes certain difficulties on both recognition of correct words and generation of candidate corrections for misspelled words. In this paper we describe a spelling correction method for Kazakh that takes advantage of both morphological analysis and noisy channel-based model. Our method outperforms both open source and commercial analogues in terms of the overall accuracy. We performed a comparative analysis of the spelling correction tools and pointed out some problems of spelling correction for agglutinative languages in general and for Kazakh in particular.
    Computational Linguistics and Intelligent Text Processing; 04/2014

Full-text (4 Sources)

Available from