. The retrieval of OCR degraded text using n-gram formulations within a probabilistic retrieval system is examined in this paper. Direct retrieval of documents using n-gram databases of 2 and 3-grams or 2, 3, 4 and 5-grams resulted in improved retrieval performance over standard (word based) queries on the same data when a level of 10 percent degradation or worse was achieved. A second method of using n-grams to identify appropriate matching and near matching terms for query expansion which also performed better than using standard queries is also described. This method was less effective than direct n-gram query formulations but can likely be improved with alternative query component weighting schemes and measures of term similarity. Finally, a web based retrieval application using n-gram retrieval of OCR text and display, with query term highlighting, of the source document image is described. 1 Introduction A major problem with retrieval of OCR text from image data is the inevitabl...

This paper addresses the mitigation of medical errors due to the confusion of sound-alike and look-alike drug names. Our approach involves application of two new methods--- one based on orthographic similarity ("lookalike ") and the other based on phonetic similarity ("sound-alike"). We present a new recall-based evaluation methodology for determining the effectiveness of different similarity measures on drug names. We show that the new orthographic measure (BI-SIM) outperforms other commonly used measures of similarity on a set containing both look-alike and sound-alike pairs, and that the feature-based phonetic approach (ALINE) outperforms orthographic approaches on a test set containing solely sound-alike confusion pairs. However, an approach that combines several different measures achieves the best results on both test sets.

In this paper we determine some limit distributions of pattern statistics in rational stochastic models, defined by means of nondeterministic weighted finite automata. We present a general approach to analyze these statistics in rational models having an arbitrary number of connected components. We explicitly establish the limit distributions in the most significant cases; these ones are characterized by a family of unimodal density functions defined by polynomials over adjacent intervals.