A Graph Auto-encoder Model of Derivational Morphology
Valentin Hofmann, Hinrich Schütze, Janet B. Pierrehumbert
2020Year
10Citations
2Top-tier citations
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 embeddings capturing information about the compatibility of affixes and stems in derivation. The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.
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Install the CLIlune papers fulltext 179774fc-1dbc-482a-919b-299010dff7a6Cited by top-tier papers2
- Predicting the Growth of Morphological Families from Social and Linguistic FactorsValentin Hofmann, Janet B. Pierrehumbert, Hinrich SchützeACL 2020 · 13 citations
- Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language ModelLeonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai et al.EMNLP 2023 · 10 citations
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