Papers by Caitlyn Chen

1 papers
FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric (2023.eacl-main)

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Challenge: Existing syntactic similarity metrics are computationally expensive and inconsistent when faced with syntaktically dissimilar documents.
Approach: They propose a metric which pairs and averages the most similar constituency parse trees between a pair of documents based on tree kernels.
Outcome: The proposed metric is more robust to syntactic dissimilarities and runs up to 5.32 times faster than its predecessor over documents in the r/ChangeMyView corpus.

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