Show simple item record Boleda, Gemma Gupta, Abhijeet Baroni, Marco Padó, Sebastian 2017-08-25T17:17:14Z 2017-08-25T17:17:14Z 2015
dc.identifier.citation Gupta A, Boleda G, Baroni M, Pado S. Distributional vectors encode referential attributes. In: Márquez L, Callison-Burch C, Su J, editors. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics; 2015. p. 12-21.
dc.description.abstract Distributional methods have proven to excel at capturing fuzzy, graded aspects of meaning (Italy is more similar to Spain than to Germany). In contrast, it is difficult to extract the values of more specific attributes of word referents from distributional representations, attributes of the kind typically found in structured knowledge bases (Italy has 60 million inhabitants). In this paper, we pursue the hypothesis that distributional vectors also implicitly encode referential attributes. We show that a standard supervised regression model is in fact sufficient to retrieve such attributes to a reasonable degree of accuracy: When evaluated on the prediction of both categorical and numeric attributes of countries and cities, the model consistently reduces baseline error by 30%, and is not far from the upper bound. Further analysis suggests that our model is able to “objectify” distributional representations for entities, anchoring them more firmly in the external world in measurable ways.
dc.description.sponsorship This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 655577 (LOVe); ERC 2011 Starting Independent Research Grant n. 283554 (COMPOSES); DFG (SFB 732, Project D10); and Spanish MINECO (grant FFI2013-41301-P).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ACL (Association for Computational Linguistics)
dc.relation.ispartof Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics; 2015. p. 12-21
dc.rights © ACL, Creative Commons Attribution-NonCommercial-ShareAlike3.0
dc.title Distributional vectors encode referential attributes
dc.type info:eu-repo/semantics/conferenceObject
dc.subject.keyword Reference
dc.subject.keyword Distributed representations
dc.subject.keyword Semantics
dc.subject.keyword Computational semantics
dc.subject.keyword Computational Linguistics
dc.subject.keyword Natural Language Processing
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/655577
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/283554
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/FFI2013-41301-P
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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