Pham, Nghia TheKruszewski, GermanLazaridou, AngelikiBaroni, Marco2020-12-152020-12-152015The Pham N, Kruszewski G, Lazaridou A, Baroni M. Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model. In: Zong C, Strube M, editors. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers); 2015 Jul 26-31; Beijing, China. Stroudsburg (PA): Association for Computational Linguistics; 2015. p. 971-81. DOI: 10.3115/v1/P15-1094http://hdl.handle.net/10230/46044Comunicació 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.application/pdfeng© ACL, Creative Commons Attribution 3.0 License (https://creativecommons.org/licenses/by-nc-sa/3.0/)info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.3115/v1/P15-1094info:eu-repo/semantics/openAccess