MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP
We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word level or sequences of words) MultiVec includes Mikolov et al. [2013b]'s word2vec features, Le and Mikolov [2014]'s paragraph vector (batch and online) and Luong et al. [2015]'s model for bilingual distributed representations. MultiVec also includes different distance measures between words and sequences of words. The toolkit is written in C+ and is aimed at being fast (in the same order of magnitude as word2vec) easy to use, and easy to extend. It has been evaluated on several NLP tasks: the analogical reasoning task, sentiment analysis, and crosslingual document classification.
LREC