A Graph Auto-encoder Model of Derivational Morphology

Hofmann, Valentin; Schütze, Hinrich and Pierrehumbert, Janet (6. July 2020): A Graph Auto-encoder Model of Derivational Morphology. , July 6 – 8, 2020, Seattle, USA

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.

• Item Type: Conference
• EU Funded Grant Agreement Number: 740516
• EU Projects: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Research Centers: Center for Information and Language Processing (CIS)
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems; 400 Language > 410 Linguistics
• Place of Publication: Stroudsburg, USA
• Language: English