10.7910/DVN/BZ1RWS Rozado, David David Rozado https://orcid.org/0000-0001-6849-4746 CSIRO Harvard Dataverse 2019 Computer and Information Science Social Sciences word2vec Model, word embeddings Rozado, David David Rozado Otago Polytechnic 2019-06-10 2019-06-10 10.7910/DVN/BZ1RWS/FDSELM 10.7910/DVN/BZ1RWS/ZOAHOI 2147483648 1145831825 application/x-rar-compressed application/x-rar-compressed 1.0 Creative Commons CC0 1.0 Universal Public Domain Dedication. word2vec model trained on the concatenation of all the individual universities corpora. To generate the word embeddings of the corpus, the gensim implementation of word2vec (CBOW) was used. For training the word embeddings model, the following parameters were used: vector dimensions=300, window size=10, negative sampling=10, down sampling frequent words = 0.00008 (downsamples 612 most-common words), number of iterations (epochs) through the corpus=10, maximum final vocabulary= 3 million. The maximum final vocabulary resulted in an effective minimum frequency count of 20. That is, only terms that appear more than 20 times in the corpus were included into the word embedding model vocabulary. The exponent used to shape the negative sampling distribution was 0.5.