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.
Similarly, REF "guide" German word embeddings with morphological annotation, and Gieske (2017) use morphological information encoded in word embeddings to inflect German verbs.
Morphological Word Embeddings
ArXiv
ArXiv
Computer Science