word2vec проапгрейдили

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:


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:

Об авторе Лидия Пивоварова

СПбГУ - старший преподаватель, University of Helsinki - PhD student http://philarts.spbu.ru/structure/sub-faculties/itah_phil/teachers/pivovarova
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