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Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
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Abstract:
Comunicació presentada a: 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing celebrat del 26 al 31 de juliol de 2015 a Pequín, Xina. ; 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. ; We thank Gemma Boleda and the anonymous reviewers for useful comments. We acknowledge ERC 2011 Starting Independent Research Grant n. 283554 (COMPOSES).
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URL: http://hdl.handle.net/10230/46044
https://doi.org/10.3115/v1/P15-1094
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