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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 – 9 of 9 1 CaMEL: Case Marker Extraction without Labels ... Weissweiler, Leonie; Hofmann, Valentin; Sabet, Masoud Jalili. - : arXiv, 2022 BASE Show details 2 Geographic Adaptation of Pretrained Language Models ... Hofmann, Valentin; Glavaš, Goran; Ljubešić, Nikola. - : arXiv, 2022 BASE Show details 3 Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Hinrich; Hofmann, Valentin. - : Underline Science Inc., 2021 BASE Show details 4 Dynamic Contextualized Word Embeddings ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Hinrich; Hofmann, Valentin. - : Underline Science Inc., 2021 BASE Show details 5 Predicting the Growth of Morphological Families from Social and Linguistic Factors Hofmann, Valentin; Schütze, Hinrich; Pierrehumbert, Janet. - : Ludwig-Maximilians-Universität München, 2020 BASE Show details 6 A Graph Auto-encoder Model of Derivational Morphology Schütze, Hinrich; Pierrehumbert, Janet; Hofmann, Valentin. - : Ludwig-Maximilians-Universität München, 2020 Abstract: There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics (Bauer, 2019). We present a graph auto-encoder that learns em- beddings capturing information about the com- patibility of affixes and stems in derivation. The auto-encoder models MWF in English sur- prisingly well by combining syntactic and se- mantic information with associative informa- tion from the mental lexicon. Keyword: ddc:000; ddc:410 URL: https://epub.ub.uni-muenchen.de/72197/1/Hofmann_etal_DGA_ACL2020.pdf http://nbn-resolving.de/urn:nbn:de:bvb:19-epub-72197-4 https://epub.ub.uni-muenchen.de/72197/ https://doi.org/10.5282/ubm/epub.72197 BASE Hide details 7 Dynamic Contextualized Word Embeddings ... Hofmann, Valentin; Pierrehumbert, Janet B.; Schütze, Hinrich. - : arXiv, 2020 BASE Show details 8 Predicting the Growth of Morphological Families from Social and Linguistic Factors ... Hofmann, Valentin; Pierrehumbert, Janet; Schütze, Hinrich. - : Association for Computational Linguistics, 2020 BASE Show details 9 A Graph Auto-encoder Model of Derivational Morphology ... Hofmann, Valentin; Schütze, Hinrich; Pierrehumbert, Janet. - : Association for Computational Linguistics, 2020 BASE Show details

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