Modelling string structure in vector spaces
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Searching for similar strings is an important and frequent database task both in terms of human interactions and in absolute world-wide CPU utilisation. A wealth of metric functions for string comparison exist. However, with respect to the wide range of classification and other techniques known within vector spaces, such metrics allow only a very restricted range of techniques. To counter this restriction, various strategies have been used for mapping string spaces into vector spaces, approximating the string distances within the mapped space and therefore allowing vector space techniques to be used. In previous work we have developed a novel technique for mapping metric spaces into vector spaces, which can therefore be applied for this purpose. In this paper we evaluate this technique in the context of string spaces, and compare it to other published techniques for mapping strings to vectors. We use a publicly available English lexicon as our experimental data set, and test two different string metrics over it for each vector mapping. We find that our novel technique considerably outperforms previously used technique in preserving the actual distance.
Connor , R , Dearle , A & Vadicamo , L 2019 , Modelling string structure in vector spaces . in M Mecella , G Amato & C Gennaro (eds) , Proceedings of the 27th Italian Symposium on Advanced Database Systems : Castiglione della Pescaia (Grosseto), Italy, June 16th to 19th, 2019 . , 45 , CEUR Workshop Proceedings , vol. 2400 , Sun SITE Central Europe , SEBD 2019 27th Italian Symposium on Advanced Database Systems , Castiglione della Pescaia , Italy , 17/06/19 . < http://ceur-ws.org/Vol-2400/paper-45.pdf >workshop
Proceedings of the 27th Italian Symposium on Advanced Database Systems
© 2019, the Author(s). This work has been made available online in accordance with the publisher's policies. This is the final published version of the work, which was originally published at http://ceur-ws.org/Vol-2400/
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