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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 OCNLI: Original Chinese Natural Language Inference ... Hu, Hai; Richardson, Kyle; Xu, Liang. - : arXiv, 2020 BASE Show details 2 MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity ... Hu, Hai; Chen, Qi; Richardson, Kyle. - : arXiv, 2019 BASE Show details 3 Detecting Syntactic Features of Translated Chinese ... Hu, Hai; Li, Wen; Kübler, Sandra. - : arXiv, 2018 Abstract: We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features. Using Support Vector Machines (SVMs) as classifier on a genre-balanced corpus in translation studies of Chinese, we find that constituent parse trees and dependency triples as features without lexical information perform very well on the task, with an F-measure above 90%, close to the results of lexical n-gram features, without the risk of learning topic information rather than translation features. Thus, we claim syntactic features alone can accurately distinguish translated from original Chinese. Translated Chinese exhibits an increased use of determiners, subject position pronouns, NP + 'de' as NP modifiers, multiple NPs or VPs conjoined by a Chinese specific punctuation, among other structures. We also interpret the syntactic features with reference to previous translation studies in Chinese, particularly the usage of ... : Accepted to 2nd Workshop on Stylistic Variation, NAACL 2018 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1804.08756 https://arxiv.org/abs/1804.08756 BASE Hide details 4 Performing Stance Detection on Twitter Data using Computational Linguistics Techniques ... Shenoy, Gourav G.; Dsouza, Erika H.; Kübler, Sandra. - : arXiv, 2017 BASE Show details

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