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.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Nghia The Pham, Germán Kruszewski, Angeliki Lazaridou, Marco Baroni
• Keywords: ACL 2015, Computational Linguistics