Fork of wang2vec with extensions for re-training and count based models and a more accurate ETA prognosis.
cd dereko2vec mkdir build cd build cmake .. make && ctest3 --extra-verbose && sudo make install
The command to build word embeddings is exactly the same as in the original version, except that we added type 5 for setting up a purely count based collocation database.
The -type argument is a integer that defines the architecture to use. These are the possible parameters:
0 - cbow
1 - skipngram
2 - cwindow (see below)
3 - structured skipngram(see below)
4 - collobert's senna context window model (still experimental)
5 - build a collocation count database instead of word embeddings
./dereko2vec -train input_file -output embedding_file -type 0 -size 50 -window 5 -negative 10 -nce 0 -hs 0 -sample 1e-4 -threads 1 -binary 1 -iter 5 -cap 0
The KorAP-XML-CoNLL-U tool can be used to generate input files for dereko2vec from KorAP-XML ZIPs using its tokenization and setence boundary information, for example:
korapxml2conllu --word2vec wpd19.zip > wpd19.w2vinput
@InProceedings{Ling:2015:naacl,
author = {Ling, Wang and Dyer, Chris and Black, Alan and Trancoso, Isabel},
title="Two/Too Simple Adaptations of word2vec for Syntax Problems",
booktitle="Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
year="2015",
publisher="Association for Computational Linguistics",
location="Denver, Colorado",
}
@InProceedings{FankhauserKupietz2019,
author = {Peter Fankhauser and Marc Kupietz},
title = {Analyzing domain specific word embeddings for a large corpus of contemporary German},
series = {Proceedings of the 10th International Corpus Linguistics Conference},
publisher = {University of Cardiff},
address = {Cardiff},
year = {2019},
note = {\url{https://doi.org/10.14618/ids-pub-9117}}
}