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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 – 3 of 3 1 Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models ... Guo, Meiqi; Hwa, Rebecca; Lin, Yu-Ru; Chung, Wen-Ting. - : arXiv, 2020 Abstract: We investigate the impact of political ideology biases in training data. Through a set of comparison studies, we examine the propagation of biases in several widely-used NLP models and its effect on the overall retrieval accuracy. Our work highlights the susceptibility of large, complex models to propagating the biases from human-selected input, which may lead to a deterioration of retrieval accuracy, and the importance of controlling for these biases. Finally, as a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance. ... : To appear in The Proceedings of The 28th International Conference on Computational Linguistics (COLING-2020) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2011.14293 https://dx.doi.org/10.48550/arxiv.2011.14293 BASE Hide details 2 Supervised Grammar Induction Using Training Data with Limited Constituent Information ... Hwa, Rebecca. - : arXiv, 1999 BASE Show details 3 An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars ... Hwa, Rebecca. - : arXiv, 1998 BASE Show details

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