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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.
| Dokumententyp: | Konferenz |
|---|---|
| EU Funded Grant Agreement Number: | 740516 |
| EU-Projekte: | Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement |
| Fakultätsübergreifende Einrichtungen: | Centrum für Informations- und Sprachverarbeitung (CIS) |
| Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
400 Sprache > 410 Linguistik |
| URN: | urn:nbn:de:bvb:19-epub-72197-4 |
| Ort: | Stroudsburg, USA |
| Sprache: | Englisch |
| Dokumenten ID: | 72197 |
| Datum der Veröffentlichung auf Open Access LMU: | 20. Mai 2020, 09:39 |
| Letzte Änderungen: | 04. Nov. 2020, 13:53 |
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