Convolutional Embedding for Edit Distance

Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng

Abstract:

Edit-distance-based string similarity search has many applications
such as spell correction, data de-duplication, and sequence alignment. However, computing edit distance is known to have high
complexity, which makes string similarity search challenging for
large datasets. In this paper, we propose a deep learning pipeline
(called CNN-ED) that embeds edit distance into Euclidean distance
for fast approximate similarity search. A convolutional neural network (CNN) is used to generate fixed-length vector embeddings
for a dataset of strings and the loss function is a combination of
the triplet loss and the approximation error. To justify our choice
of using CNN instead of other structures (e.g., RNN) as the model,
theoretical analysis is conducted to show that some basic operations in our CNN model preserve edit distance. Experimental results
show that CNN-ED outperforms data-independent CGK embedding and RNN-based GRU embedding in terms of both accuracy
and efficiency by a large margin. We also show that string similarity search can be significantly accelerated using CNN-based
embeddings, sometimes by orders of magnitude.