String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Recognition and parsing of context-free languages in time n3*

Abstract: A recognition algorithm is exhibited whereby an arbitrary string over a given vocabulary can be tested for containment in a given context-free language. A special merit of this algorithm is that it is completed in a number of steps proportional to the “cube” of the number of symbols in the tested string. As a byproduct of the grammatical analysis, required by the recognition algorithm, one can obtain, by some additional processing not exceeding the “cube” factor of computational complexity, a parsing matrix—a complete summary of the grammatical structure of the sentence. It is also shown how, by means of a minor modification of the recognition algorithm, one can obtain an integer representing the ambiguity of the sentence, i.e., the number of distinct ways in which that sentence can be generated by the grammar. The recognition algorithm is then simulated on a Turing Machine. It is shown that this simulation likewise requires a number of steps proportional to only the “cube” of the test string length.

Text Mining Infrastructure in R

Abstract: During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

Adaptive duplicate detection using learnable string similarity measures

Abstract: The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.

STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene

Abstract: The repeated occurrence of genes in each other’s neighbourhood on genomes has been shown to indicate a functional association between the proteins they encode. Here we introduce STRING (search tool for recurring instances of neighbouring genes), a tool to retrieve and display the genes a query gene repeatedly occurs with in clusters on the genome. The tool performs iterative searches and visualises the results in their genomic context. By finding the genomically associated genes for a query, it delineates a set of potentially functionally associated genes. The usefulness of STRING is illustrated with an example that suggests a functional context for an RNA methylase with unknown specificity. STRING is available at http://www.bork.embl-heidelberg.de/STRING

A Syntax-based Statistical Translation Model

Abstract: We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5.