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ISCApad Archive  »  2017  »  ISCApad #229  »  Resources  »  Software  »  MultiVec: a Multilingual and MultiLevel Representation Learning Toolkit for NLP

ISCApad #229

Monday, July 10, 2017 by Chris Wellekens

5-3-7 MultiVec: a Multilingual and MultiLevel Representation Learning Toolkit for NLP
  

 

We are happy to announce the release of our new toolkit “MultiVec” 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. The toolkit also includes C++ and Python libraries, that you can use to query bilingual and monolingual models.

 

The project is fully open to future contributions. The code is provided on the project webpage (https://github.com/eske/multivec) with installation instructions and command-line usage examples.

 

When you use this toolkit, please cite:

 

@InProceedings{MultiVecLREC2016,

Title                    = {{MultiVec: a Multilingual and MultiLevel Representation Learning Toolkit for NLP}},

Author                   = {Alexandre Bérard and Christophe Servan and Olivier Pietquin and Laurent Besacier},

Booktitle                = {The 10th edition of the Language Resources and Evaluation Conference (LREC 2016)},

Year                     = {2016},

Month                    = {May}

}

 

The paper is available here: https://github.com/eske/multivec/raw/master/docs/Berard_and_al-MultiVec_a_Multilingual_and_Multilevel_Representation_Learning_Toolkit_for_NLP-LREC2016.pdf

 

Best regards,

 

Alexandre Bérard, Christophe Servan, Olivier Pietquin and Laurent Besacier


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