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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Detecting de minimis Code-Switching in Historical German Books ... The 28th International Conference on Computational Linguistics 2020; Liu, Shijia; Smith, David. - : Underline Science Inc., 2020 BASE Show details 2 Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions McCarthy, Arya D.; Cotterell, Ryan; Williams, Adina... In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 3 Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions ... McCarthy, Arya D.; Williams, Adina; Liu, Shijia. - : ETH Zurich, 2020 BASE Show details 4 On the Idiosyncrasies of the Mandarin Chinese Classifier System ... Liu, Shijia; Mei, Hongyuan; Williams, Adina; Cotterell, Ryan. - : arXiv, 2019 Abstract: While idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lakoff, 1986), not much work has been done to quantify them with statistical methods. In this paper, we introduce an information-theoretic approach to measuring idiosyncrasy; we examine how much the uncertainty in Mandarin Chinese classifiers can be reduced by knowing semantic information about the nouns that the classifiers modify. Using the empirical distribution of classifiers from the parsed Chinese Gigaword corpus (Graff et al., 2005), we compute the mutual information (in bits) between the distribution over classifiers and distributions over other linguistic quantities. We investigate whether semantic classes of nouns and adjectives differ in how much they reduce uncertainty in classifier choice, and find that it is not fully idiosyncratic; while there are no obvious trends for the majority of semantic classes, shape nouns reduce uncertainty in classifier ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1902.10193 https://arxiv.org/abs/1902.10193 BASE Hide details

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