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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 – 14 of 14 1 Semantic changes in harm-related concepts in English ... Vylomova, Ekaterina; Haslam, Nick. - : Zenodo, 2021 BASE Show details 2 Semantic changes in harm-related concepts in English ... Vylomova, Ekaterina; Haslam, Nick. - : Zenodo, 2021 BASE Show details 3 More confident, less formal: stylistic changes in academic psychology writing from 1970 to 2016 Wheeler, Melissa A.; Vylomova, Ekaterina; McGrath, Melanie J.... In: Scientometrics, Vol. 126, no. 12 (Dec 2021), pp. 9603-9612 (2021) BASE Show details 4 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 5 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 6 UniMorph 3.0: Universal Morphology Kirov, Christo; Nicolai, Garrett; Arkhangelskij, Timofey... In: Proceedings of the 12th Language Resources and Evaluation Conference (2020) BASE Show details 7 UniMorph 3.0: Universal Morphology ... McCarthy, Arya D.; Kirov, Christo; Grella, Matteo. - : ETH Zurich, 2020 BASE Show details 8 Harm inflation: Making sense of concept creep Weaving, Morgan; Wheeler, Melissa A.; Fabiano, Fabian... In: European Review of Social Psychology, Vol. 31, no. 1 (Jan 2020), pp. 254-286 (2020) BASE Show details 9 Contextualization of Morphological Inflection ... Vylomova, Ekaterina; Cotterell, Ryan; Baldwin, Timothy. - : arXiv, 2019 BASE Show details 10 The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ... McCarthy, Arya D.; Vylomova, Ekaterina; Wu, Shijie. - : arXiv, 2019 BASE Show details 11 Compositional morphology through deep learning Vylomova, Ekaterina. - 2018 Abstract: © 2018 Dr. Ekaterina Vylomova ; Most human languages have sophisticated morphological systems. In order to build successful models of language processing, we need to focus on morphology, the internal structure of words. In this thesis, we study two morphological processes: inflection (word change rules, e.g. run -- runs) and derivation (word formation rules, e.g. run -- runner). We first evaluate the ability of contemporary models that are trained using the distributional hypothesis, which states that a word's meaning can be expressed by the context in which it appears, to capture these types of morphology. Our study reveals that inflections are predicted at high accuracy whereas derivations are more challenging due to irregularity of meaning change. We then demonstrate that supplying the model with character-level information improves predictions and makes usage of language resources more efficient, especially in morphologically rich languages. We then address the question of to what extent and which information about word properties (such as gender, case, number) can be predicted entirely from a word's sentential content. To this end, we introduce a novel task of contextual inflection prediction. Our experiments on prediction of morphological features and a corresponding word form from sentential context show that the task is challenging, and as morphological complexity increases, performance significantly drops. We found that some morphological categories (e.g., verbal tense) are inherent and typically cannot be predicted from context while others (e.g., adjective number and gender) are contextual and inferred from agreement. Compared to morphological inflection tasks, where morphological features are explicitly provided, and the system has to predict only the form, accuracy on this task is much lower. Finally, we turn to word formation, derivation. Experiments with derivations show that they are less regular and systematic. We study how much a sentential context is indicative of a meaning change type. Our results suggest that even though inflections are more productive and regular than derivations, the latter also present cases of high regularity of meaning and form change, but often require extra information such as etymology, word frequency, and more fine-grained annotation in order to be predicted at high accuracy. Keyword: deep learning; language model; machine translation; morphology model; natural language processing URL: http://hdl.handle.net/11343/224349 BASE Hide details 12 Context-Aware Prediction of Derivational Word-forms ... Vylomova, Ekaterina; Cotterell, Ryan; Baldwin, Timothy. - : arXiv, 2017 BASE Show details 13 Paradigm Completion for Derivational Morphology ... Cotterell, Ryan; Vylomova, Ekaterina; Khayrallah, Huda. - : arXiv, 2017 BASE Show details 14 Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning ... Vylomova, Ekaterina; Rimell, Laura; Cohn, Trevor. - : arXiv, 2015 BASE Show details

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