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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 – 5 of 5 1 Gender Bias in Text: Labeled Datasets and Lexicons ... Doughman, Jad; Khreich, Wael. - : arXiv, 2022 BASE Show details 2 Gender Bias in Text: Labeled Datasets and Lexicons ... Doughman, Jad; Khreich, Wael. - : Zenodo, 2022 BASE Show details 3 Gender Bias in Text: Labeled Datasets and Lexicons ... Doughman, Jad; Khreich, Wael. - : Zenodo, 2022 BASE Show details 4 Gender Bias in Text: Labeled Datasets and Lexicons ... Doughman, Jad; Khreich, Wael. - : Zenodo, 2022 BASE Show details 5 DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings ... Abdul-Mageed, Muhammad; Elbassuoni, Shady; Doughman, Jad; Elmadany, AbdelRahim; Nagoudi, El Moatez Billah; Zoughby, Yorgo; Shaher, Ahmad; Gaba, Iskander; Helal, Ahmed; El-Razzaz, Mohammed. - : arXiv, 2020 Abstract: Word embeddings are a core component of modern natural language processing systems, making the ability to thoroughly evaluate them a vital task. We describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word embedding. DiaLex covers five important Arabic dialects: Algerian, Egyptian, Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a testbank for six syntactic and semantic relations, namely male to female, singular to dual, singular to plural, antonym, comparative, and genitive to past tense. DiaLex thus consists of a collection of word pairs representing each of the six relations in each of the five dialects. To demonstrate the utility of DiaLex, we use it to evaluate a set of existing and new Arabic word embeddings that we developed. Our benchmark, evaluation code, and new word embedding models will be publicly available. ... : WANLP2021 ... Keyword: Artificial Intelligence cs.AI; FOS Computer and information sciences URL: https://arxiv.org/abs/2011.10970 https://dx.doi.org/10.48550/arxiv.2011.10970 BASE Hide details

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