Speakers of a language can construct an unlimited number of new words through morphological derivation. This is a major cause of data sparseness for corpus-based approaches to lexical semantics, such as distributional semantic models of word meaning. We adapt compositional methods originally developed for phrases to the task of deriving the distributional meaning of morphologically complex words from their parts. Semantic representations constructed in this way beat a strong baseline and can be of higher quality than representations directly constructed from corpus data. Our results constitute a novel evaluation of the proposed composition methods, in which the full additive model achieves the best performance, and demonstrate the usefulness of a compositional morphology component in distributional semantics.
Lazaridou et al. REF use compositional distributional semantic models, originally designed to learn meanings of phrases, to derive representations for complex words, in which the base unit is the morpheme.
Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics
ACL
Computer Science