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arXiv Computation and Language By Shijia Liu, Hongyuan Mei, Adina Williams, Ryan Cotterell

On the Idiosyncrasies of the Mandarin Chinese Classifier System

Read the original on arXiv Computation and Language →

The paper introduces an information‑theoretic method to quantify idiosyncrasy in Mandarin Chinese classifiers, measuring how much knowing noun semantics reduces uncertainty in classifier choice. Using the Chinese Gigaword corpus, the authors compute mutual information between classifier distributions and other linguistic variables. They find that, overall, classifier choice is not fully idiosyncratic; shape nouns, in particular, reduce uncertainty the most, while other semantic classes show no clear trend.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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