Topic
Approximate string matching
About: Approximate string matching is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 62352 citations. The topic is also known as: fuzzy string-searching algorithm & fuzzy string-matching algorithm.
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14 May 2019
TL;DR: A novel method for addressing 'Fuzzy Matching', which exploits the fact most server-class CPUs include vector operations to parallelize message matching and allows matches based on 'partial truth', i.e., by identifying probable rather than exact matches.
Abstract: Contemporary parallel scientific codes often rely on message passing for inter-process communication. However, inefficient coding practices or multithreading (e.g., via MPI_THREAD_MULTIPLE) can severely stress the underlying message processing infrastructure, resulting in potentially un-acceptable impacts on application performance. In this article, we propose and evaluate a novel method for addressing this issue: 'Fuzzy Matching'. This approach has two components. First, it exploits the fact most server-class CPUs include vector operations to parallelize message matching. Second, based on a survey of point-to-point communication patterns in representative scientific applications, the method further increases parallelization by allowing matches based on 'partial truth', i.e., by identifying probable rather than exact matches. We evaluate the impact of this approach on memory usage and performance on Knight's Landing and Skylake processors. At scale (262,144 Intel Xeon Phi cores), the method shows up to 1.13 GiB of memory savings per node in the MPI library, and improvement in matching time of 95.9%; smaller-scale runs show run-time improvements of up to 31.0% for full applications, and up to 6.1% for optimized proxy applications.
11 citations
14 Sep 2004
TL;DR: There is no known algorithm that solves the general case of approximate string matching problem with the extended edit distance, where the edit operations are: insertion, deletion, mismatch, and swap, in time o(nm), where n is the length of the text and m is thelength of the pattern.
Abstract: There is no known algorithm that solves the general case of approximate string matching problem with the extended edit distance, where the edit operations are: insertion, deletion, mismatch, and swap, in time o(nm), where n is the length of the text and m is the length of the pattern.
11 citations
Journal Article•
TL;DR: Gapped q-grams as discussed by the authors are subsets of q characters in some fixed non-contiguous shape, instead of contiguous substrings, which can provide orders of magnitude faster and/or more efficient filtering than contiguous qgrams.
Abstract: The q-gram filter is a popular filtering method for approximate string matching. It compares substrings of length q (the q-grams) in the pattern and the text to identify the text areas that might contain a match. A generalization of the method is to use gapped q-grams, subsets of q characters in some fixed non-contiguous shape, instead of contiguous substrings. Although mentioned a few times in the literature, this generalization has never been studied in any depth. In this paper, we report the first results from a study on gapped q-grams. We show that gapped q-grams can provide orders of magnitude faster and/or more efficient filtering than contiguous q-grams. The performance, however, depends on the shape of the q-grams. The best shapes are rare and often possess no apparent regularity. We show how to recognize good shapes and demonstrate with experiments their advantage over both contiguous and average shapes. We concentrate here on the k mismatches problem, but also outline an approach for extending the results to the more common k differences problem.
11 citations
TL;DR: Out outliers help model problems associated with using biological data, but the problem of finding an approximate solution is computationally difficult, and it is shown the problem has no PTAS unless P=NP.
11 citations
TL;DR: In this article, the authors evaluate various forms of fuzzy string matching between participants' responses and target sentences, as automated metrics of listener transcript accuracy, and demonstrate that one particular metric, the token sort ratio, is a consistent, highly efficient, and accurate metric for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores.
Abstract: Many studies of speech perception assess the intelligibility of spoken sentence stimuli by means of transcription tasks (‘type out what you hear’). The intelligibility of a given stimulus is then often expressed in terms of percentage of words correctly reported from the target sentence. Yet scoring the participants’ raw responses for words correctly identified from the target sentence is a time-consuming task, and hence resource-intensive. Moreover, there is no consensus among speech scientists about what specific protocol to use for the human scoring, limiting the reliability of human scores. The present paper evaluates various forms of fuzzy string matching between participants’ responses and target sentences, as automated metrics of listener transcript accuracy. We demonstrate that one particular metric, the token sort ratio, is a consistent, highly efficient, and accurate metric for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores (best correlation: r = 0.940) and a strong relationship to acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for assessment of listener transcript accuracy in large-scale speech intelligibility studies. See https://tokensortratio.netlify.app
for an online implementation.
11 citations