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ACL
2015

Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model

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Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
We introduce C-PHRASE, a distributional semantic model that learns word representations by optimizing context prediction for phrases at all levels in a syntactic tree, from single words to full sentences. C-PHRASE outperforms the state-of-theart C-BOW model on a variety of lexical tasks. Moreover, since C-PHRASE word vectors are induced through a compositional learning objective (modeling the contexts of words combined into phrases), when they are summed, they produce sentence representations that rival those generated by ad-hoc compositional models.
Nghia The Pham, Germán Kruszewski, Angeliki
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Nghia The Pham, Germán Kruszewski, Angeliki Lazaridou, Marco Baroni
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