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.
Cite
CITATION STYLE
Hofmann, V., Schütze, H., & Pierrehumbert, J. B. (2020). A graph auto-encoder model of derivational morphology. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 1127–1138). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-main.106
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