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Measuring similarities between strings is central for many established andfast growing research areas including information retrieval, biology, andnatural language processing. The traditional approach for string similaritymeasurements is to define a metric over a word space that quantifies and sumsup the differences between characters in two strings. The state-of-the-art inthe area has, surprisingly, not evolved much during the last few decades. Themajority of the metrics are based on a simple comparison between character andcharacter distributions without consideration for the context of the words.This paper proposes a string metric that encompasses similarities betweenstrings based on (1) the character similarities between the words including.Non-Standard and standard spellings of the same words, and (2) the context ofthe words. Our proposal is a neural network composed of a denoising autoencoderand what we call a context encoder specifically designed to find similaritiesbetween the words based on their context. The experimental results show thatthe resulting metrics succeeds in 85.4\% of the cases in finding the correctversion of a non-standard spelling among the closest words, compared to 63.2\%with the established Normalised-Levenshtein distance. Besides, we show thatwords used in similar context are with our approach calculated to be similarthan words with different contexts, which is a desirable property missing inestablished string metrics.

Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String Similarities