@article{409e3a31-0824-47ab-bbc3-cb1b8b3e671d,
author = {Grzegorz Jurdziński },
title = {Word Embeddings for Morphologically Complex Languages},
journal = {Schedae Informaticae},
volume = {2016},
number = {Volume 25},
year = {2017},
issn = {1732-3916},
pages = {127-138},keywords = {machine learning; word embeddings; natural language processing; morphology 1. Introduction},
abstract = {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.},
doi = {10.4467/20838476SI.16.010.6191},
url = {https://ejournals.eu/en/journal/schedae-informaticae/article/word-embeddings-for-morphologically-complex-languages}
}