%0 Journal Article
%T Word Embeddings for Morphologically Complex Languages
%A Jurdziński, Grzegorz
%J Schedae Informaticae
%V 2016
%R 10.4467/20838476SI.16.010.6191
%N Volume 25
%P 127-138
%K machine learning, word embeddings, natural language processing, morphology 1. Introduction
%@ 1732-3916
%D 2017
%U https://ejournals.eu/en/journal/schedae-informaticae/article/word-embeddings-for-morphologically-complex-languages
%X 
	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.