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

TL;DR

This paper introduces a novel neural network-based string similarity metric that incorporates character-level and contextual information, outperforming traditional methods like Normalised-Levenshtein distance in accuracy and contextual relevance.

Contribution

It proposes a new string similarity measure combining a denoising autoencoder and a context encoder, capturing both character similarities and contextual relationships.

Findings

Achieves 85.4% accuracy in identifying correct non-standard spellings.

Outperforms Normalised-Levenshtein distance by 22.2% in accuracy.

Words in similar contexts are correctly identified as similar.

Abstract

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