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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 Page: 1 2 Hits 1 – 20 of 21 1 An Analysis of Gender Bias in K-12 Assigned Literature Through Comparison of Non-Contextual Word Embedding Models Mohan, Preeti. - 2021 Abstract: Thesis (Master's)--University of Washington, 2021 ; Word embeddings are mathematical representations of words computed from a group of texts that a machine learning model is trained on. Generally, words that are similar to each othersemantically will be closer together in the vector-space created by the embedding model. The distance between words can be analyzed to understand what words tend to be used in the same contexts in a given group of texts. In this thesis, I use three different non-contextual methods of training word embedding models, Word2Vec (Mikolov et al., 2013), FastText (Bojanowski et al., 2017), and GloVe (Pennington et al., 2014), on a corpus of literature assigned to students in grades K-12 in the United States to answer three questions:- It has been shown that children are particularly prone to internalize biases in thecontent they read and watch (Railsback, 1993; Jacobs, 2003; Slater, 2003). What biases are present in literature assigned to children in grades K-12 in the United States? - Are different kinds of non-contextual word embeddings sensitive to bias in different ways? -Is the text from one book enough to detect bias using non-contextual word embedding models? I find that GloVe embeddings are more sensitive to biases in smaller corpora, while Word2Vec and FastText are more sensitive to biases in large corpora. When looking at the word embeddings from a single book, I see variations in the strength of the words that are the “most gendered” — a book that had stronger gender biases (determined through literary critiques) had words that were more strongly gendered than a book that subverted gender biases (also determined through literary critique). Keyword: Children's Literature; Computer science; Linguistics; Literary Bias; Machine Learning Bias; Natural Language Processing; Social Bias; Social research; Word Embeddings URL: http://hdl.handle.net/1773/46827 BASE Hide details 2 Tracing and Reducing Lexical Ambiguity in Automatically Inferred Grammars Conrad, Elizabeth. - 2021 BASE Show details 3 A Finite-State Morphological Analyzer for Central Alaskan Yup'ik Strunk, Lonny. - 2020 BASE Show details 4 Linguistic fundamentals for natural language processing II: 100 essentials from semantics and pragmatics Bender, Emily M; Lascarides, Alex; Hirst, Graeme. - : Morgan & Claypool Publishers, 2019 BASE Show details 5 Multi-predicate Constructions in Nuuchahnulth Inman, David Anthony. - 2019 BASE Show details 6 Braiding Language (by Computer): Lushootseed Grammar Engineering Crowgey, Joshua. - 2019 BASE Show details 7 A Parametric Implementation of Valence-changing Morphology in the LinGO Grammar Matrix Curtis, Christian Michael. - 2018 BASE Show details 8 Semantic Operations for Transfer-based Machine Translation Goodman, Michael Wayne. - 2018 BASE Show details 9 Modeling Adnominal Possession in the LinGO Grammar Matrix Nielsen, Elizabeth K. - 2018 BASE Show details 10 An Evidentiality Library for the LinGO Grammar Matrix Haeger, Michael. - 2017 BASE Show details 11 Automated Gloss Mapping for Inferring Grammatical Properties Lockwood, Michael. - 2016 BASE Show details 12 Full Forest Treebanking Packard, Woodley. - 2015 BASE Show details 13 Adjectives in the LinGO Grammar Matrix Trimble, Thomas James. - 2014 BASE Show details 14 Automated Grammar Engineering for Verbal Morphology Wax, David Allen. - 2014 BASE Show details 15 A Grammar Library for Information Structure Song, Sanghoun. - 2014 BASE Show details 16 Markers of contrast in Russian: A corpus-based study Gracheva, Varya. - 2013 BASE Show details 17 Deriving a lexicon for a precision grammar from language documentation resources: a case study of Chintang ... Bender, Emily M; Schikowski, Robert; Bickel, Balthasar. - : Association for Computational Linguistics, 2012 BASE Show details 18 Deriving a lexicon for a precision grammar from language documentation resources: a case study of Chintang Schikowski, Robert; Bickel, Balthasar; Bender, Emily M In: Bender, Emily M; Schikowski, Robert; Bickel, Balthasar (2012). Deriving a lexicon for a precision grammar from language documentation resources: a case study of Chintang. In: Kay, M; Boitet, C. Proceedings of the 24th International Conference on Computational Linguistics (COLING). Mumbai: Association for Computational Linguistics, 247-262. (2012) BASE Show details 19 An analysis of translation divergence patterns using PanLex translation pairs Gola, Francesca. - 2012 BASE Show details 20 The Syntactic Exponence of Sentential Negation: a model for the LinGO Grammar Matrix Crowgey, Joshua David. - 2012 BASE Show details Page: 1 2

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