We study the problem of recognizing a string Y which is the noisy version of some unknown string X* chosen from a finite dictionary, H. The traditional case which has been extensively studied in the literature is the one in which Y contains substitution, insertion and deletion (SID) errors. Although some work has been done to extend the traditional set of edit operations to include the straightforward transposition of adjacent characters 2 [LW75] the problem is unsolved when the transposed characters are themselves subsequently substituted, as is typical in cursive and typewritten script, in molecular biology and in noisy chain-coded boundaries. In this paper we present the first reported solution to the analytic problem of editing one string X to another, Y using these four edit operations. A scheme for obtaining the optimal edit operations has also been given. Both these solutions are optimal for the infinite alphabet case. Using these algorithms we present a syntactic pattern reco...

Abstract: The errors usually made by authors during text preparation are classified. The notion of semantic errors is elaborated, and malapropisms are pointed among them as “similar ” to the intended word but essentially distorting the meaning of the text. For whatever method of malapropism correction, we propose to beforehand compile dictionaries of paronyms, i.e. of words similar to each other in letters, sounds or morphs. The proposed classification of errors and paronyms is illustrated by English and Russian examples remaining valid for many languages. Specific dictionaries of literal and morphemic paronyms are compiled for Russian. It is shown that literal paronyms drastically cut down (up to 340 times) the search of correction candidates, while morphemic paronyms permit to correct errors characteristic for foreigners and not studied so far.

This patent concerns the traditional problem encountered in the syntactic Pattern Recognition (PR) of strings or sequences. The primary investigator 1 involved in this work is a Full Professor at Carleton University in Ottawa, Canada, and is a Fellow of the IEEE. The primary problem solved by the invention involves determining the string or sequence that is most similar to a sequence presented to the system. The search could be initiated by presenting, to the system, a noisy or inexact version of a string contained in memory-for example, at a web-site or in the library or database. The invention will yield the closest string/sequence by searching the dictionary of possible words using a newly invented AIbased strategy. The core of this invention is this search strategy, called the Clustered Beam Search. Experiments have been done to show the benefits of the CBS over the current state-ofthe-art, and the results demonstrate an unbelievably marked improvement (sometimes as high as 90%) for large libraries and databases. The solution provided by the invention would be applicable in numerous areas including: Inexact or proximity searching on the Internet, keyword-based search in libraries and databases, spelling correction, speech and character recognition (including optical character recognition), and the processing of biological sequences, for example, in human genome projects. These applications are briefly described below.

[141 R. Taniguchi et al., "Picture understanding and retrieving system