Summary of the paper

Title MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP
Authors Alexandre Berard, Christophe Servan, Olivier Pietquin and Laurent Besacier
Abstract We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes word2vec's features, paragraph vector (batch and online) and bivec 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.
Topics Multilinguality, Tools, Systems, Applications, Statistical and Machine Learning Methods
Full paper MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP
Bibtex @InProceedings{BERARD16.666,
  author = {Alexandre Berard and Christophe Servan and Olivier Pietquin and Laurent Besacier},
  title = {MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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