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arXiv · 2203.08299

FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric

Maximillian Chen·Caitlyn Chen·Xiao Yu·Zhou Yu
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Abstract

Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance- and document-level. The existing standard document-level syntactic similarity metric is computationally expensive and performs inconsistently when faced with syntactically dissimilar documents. To address these challenges, we present FastKASSIM, a metric for utterance- and document-level syntactic similarity which pairs and averages the most similar constituency parse trees between a pair of documents based on tree kernels. FastKASSIM is more robust to syntactic dissimilarities and runs up to to 5.32 times faster than its predecessor over documents in the r/ChangeMyView corpus. FastKASSIM's improvements allow us to examine hypotheses in two settings with large documents. We find that syntactically similar arguments on r/ChangeMyView tend to be more persuasive, and that syntax is predictive of authorship attribution in the Australian High Court Judgment corpus.

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Maximillian Chen, Caitlyn Chen, Xiao Yu, Zhou Yu. 2022-03-15. FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric. https://arxiv.org/abs/2203.08299

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