Morphological Word-Embeddings
Ryan Cotterell, Hinrich Schütze · 2015
Linguistic similarity is multi-faceted.For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia.Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree.This work considers guiding word-embeddings with morphologically annotated data, a form of semisupervised learning, encouraging the vectors to encode a word's morphology, i.e., words close in the embedded space share morphological features.We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study.