Dear All,
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> 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_M ultilingual_and_Multilevel_Representation_Learning_Toolkit_for_NLP-LREC2016. pdf> https://github.com/eske/multivec/raw/master/docs/Berard_and_al-MultiVec_a_Mu ltilingual_and_Multilevel_Representation_Learning_Toolkit_for_NLP-LREC2016.p df Best regards, Alexandre Bérard, Christophe Servan, Olivier Pietquin and Laurent Besacier (With apologies for cross-posting)
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