Time-shifting word2vec models based on Times news paper. These models were generated using the "Generate time shifting models" scripts found here. In summary, these scripts generate a collection of sentences for every 2 years period, and trains a word2vec model on this period using gensim. The original text from the Times news paper articles is processed as follows: Articles are divided into sentences using punctuation. Punctuation symbols are removed. Text is converted to lower case. Word are validated to ensure they are valid English non-stop words (using nltk). The two year time period was selected following the Measure convergence for a range described here. This data publication was made possible thanks to collaboration with the Utrecht Digital Humanities Lab. Unfortunately original Times data set is not publicly available.

{"references": ["https://doi.org/10.5281/zenodo.1187090"]}