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Optimal Transport-based Alignment of Learned Character Representations for String Similarity

Authors :
Tam, Derek
Monath, Nicholas
Kobren, Ari
Traylor, Aaron
Das, Rajarshi
McCallum, Andrew
Tam, Derek
Monath, Nicholas
Kobren, Ari
Traylor, Aaron
Das, Rajarshi
McCallum, Andrew
Publication Year :
2019

Abstract

String similarity models are vital for record linkage, entity resolution, and search. In this work, we present STANCE --a learned model for computing the similarity of two strings. Our approach encodes the characters of each string, aligns the encodings using Sinkhorn Iteration (alignment is posed as an instance of optimal transport) and scores the alignment with a convolutional neural network. We evaluate STANCE's ability to detect whether two strings can refer to the same entity--a task we term alias detection. We construct five new alias detection datasets (and make them publicly available). We show that STANCE or one of its variants outperforms both state-of-the-art and classic, parameter-free similarity models on four of the five datasets. We also demonstrate STANCE's ability to improve downstream tasks by applying it to an instance of cross-document coreference and show that it leads to a 2.8 point improvement in B^3 F1 over the previous state-of-the-art approach.<br />Comment: ACL Long Paper

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1228358260
Document Type :
Electronic Resource