NEWS.md
In word2vec: Distributed Representations of Words

Drop C++11 specification in Makevars
Building a word2vec model is now possible by providing a list of tokenised sentences (issue #14)
word2vec is now a generic function with 2 implemented methods: word2vec.character and word2vec.list
The embeddings with the file-based (word2vec.character) and list-based approach (word2vec.list) are proven to be the same if the tokenisation is the same and the hyperparameters of the model are the same
In order to make sure the embeddings are the same the vocabulary had to be sorted according to the number of times it appears in the corpus as well as the token itself in case the number of times the 2 tokens occur is the same. This has as a consequence that the embeddings generated with version 0.4.0 will be slightly different as the ones obtained with package version < 0.4.0 due to a possible ordering difference in the vocabulary
examples provided in the help of ?word2vec and in the README
writing text data to files before training for the file-based approach (word2vec.character) now uses useBytes = TRUE (see issue #7)

Remove LazyData from DESCRIPTION as there is no data to be lazy about
Add option type to word2vec_similarity to allow both 'dot' similarity which is the default as 'cosine' similarity (requested in issue #5)

Extended predict.w2v with nearest if you pass on a vector or matrix. This allows to perform word2vec analogies or extract other similarities.
Added word2vec_similarity
Change classes returned by word2vec to 'word2vec_trained' and read.word2vec to 'word2vec'
Add detailed docs of predict.word2vec and as.matrix.word2vec
Added normalize option in read.word2vec usefull when wanting to extract the raw embedding (e.g. trained with other software)
By default models trained with version 0.2 of this R package do normalization upfront before saving the model. For version 0.1 of this package this was not the case so load these in with option normalize set to TRUE
Use Rcpp::runif as initialiser of embeddings instead of std::mt19937_64
Functionalities default usage assumes UTF-8 encoding and predict.w2v now returns character text instead of factors
Added read.wordvectors