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Word Embeddings for Morphologically Complex Languages

Jurdziński G.

Schedae Informaticae

2016

Vol. 25

127--138

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.