Word Embeddings for Morphologically Complex Languages
Grzegorz Jurdzinski · Schedae Informaticae · 2017
Recent methods for learning word embeddings, like GloVe or Word2-Vec, succeeded in spatial representation of semantic and syntactic relations.We extend GloVe by introducing separate vectors for base form and grammatical form of a word, using morphosyntactic dictionary for this.This allows vectors to capture properties of words better.We also present model results for word analogy test and introduce a new test based on WordNet.