Modelling string structure in vector spaces

Citation

Connor R, Dearle A & Vadicamo L (2019) Modelling string structure in vector spaces. In: Amato G, Mecella M & Gennaro C (eds.) 27th Italian Symposium on Advanced Database Systems. CEUR Workshop Proceedings, 2400. SEBD 2019: Italian Symposium on Advanced Database Systems, Castiglione della Pescaia (Grosseto), Italy, 16.06.2019-19.06.2019. Aachen: CEUR-WS. http://ceur-ws.org/Vol-2400/paper-45.pdf

Abstract
Searching for similar strings is an important and frequent database task both in terms of human interactions and in absolute worldwide 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.

Keywords
Metric Mapping; n-Simplex projection; Pivoted embedding; String; Jensen-Shannon distance; Levenshtein distance

• Published version