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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 13 of 13 1 Using unmarked contexts in nominal lexical semantic classification Romeo, Lauren; Mendes, Sara; Bel, Núria. - : Dublin City University and Association for Computational Linguistics, 2018 BASE Show details 2 A cascade approach to complex-type classification Bel, Núria; Romeo, Lauren; Mendes, Sara. - : European Language Resources Association, 2018 BASE Show details 3 The Structure of the lexicon in the task of the automatic acquisition of lexical information Romeo, Lauren Michele. - : Universitat Pompeu Fabra, 2015 In: TDX (Tesis Doctorals en Xarxa) (2015) BASE Show details 4 Automatic lexical semantic classification of nouns ... Bel, Núria; Romeo, Lauren; Padró, Muntsa. - : arXiv, 2013 BASE Show details 5 Using qualia information to identify lexical semantic classes in an unsupervised clustering task ... Romeo, Lauren; Mendes, Sara; Bel, Núria. - : arXiv, 2013 BASE Show details 6 Choosing which to use? A study of distributional models for nominal lexical semantic classification Romeo, Lauren; Lebani, Gianluca E.; Bel Rafecas, Núria. - : ACL (Association for Computational Linguistics) BASE Show details 7 Towards the automatic classification of complex-type nominals Romeo, Lauren; Mendes, Sara; Bel Rafecas, Núria. - : ACL (Association for Computational Linguistics) BASE Show details 8 A cascade approach for complex-type classification Romeo, Lauren; Mendes, Sara; Bel Rafecas, Núria. - : ACL (Association for Computational Linguistics) BASE Show details 9 Automatic Lexical Semantic Classification of Nouns Padró, Muntsa; Bel Rafecas, Núria; Romeo, Lauren. - : ELRA (European Language Resources Association) BASE Show details 10 Using qualia information to identify lexical semantic classes in an unsupervised clustering task Bel Rafecas, Núria; Romeo, Lauren; Mendes, Sara. - : ACL (Association for Computational Linguistics) BASE Show details 11 Structure of lexicon in the automatic acquisition of lexical information [research data] Romeo, Lauren Michele BASE Show details 12 Using unmarked contexts in nominal lexical semantic classification Romeo, Lauren; Mendes, Sara; Bel Rafecas, Núria. - : Association for Computational Linguistics (ACL) Abstract: Comunicació presentada a: COLING 2014, the 25th International Conference on Computational Linguistics, celebrat a Dublin (Irlanda), del 23 al 29 d'agost de 2014. ; The work presented here addresses the use of unmarked contexts in pattern-based nominal lexical semantic classification. We define unmarked contexts to be the counterposition of the class-indicatory, or marked, contexts. Its aim is to evaluate how unmarked contexts can be used to improve the accuracy and reliability of lexical semantic classifiers. Results demonstrate that the combined use of both types of distributional information (marked and unmarked) is crucial to improve classification. This result was replicated using two different corpora, demonstrating the robustness of the method proposed. ; This work was funded with the support of the SUR of the DEC of the Generalitat de Catalunya and the European Social Fund, by SKATER TIN2012-38584-C06-05, and by Fundação para a Ciência e a Tecnologia (FCT) post-doctoral fellowship SFRH/BPD/79900/2011. Keyword: Catálogos; Catalogues; Centros de competencias; Clarin IULA-UPF; Competence centres; Datos enlazados; Digital humanities; Humanidades digitales; Linked data; Servicios web; Web services URL: http://hdl.handle.net/10230/32683 BASE Hide details 13 Class-based word sense induction for dot-type nominals Romeo, Lauren; Bel Rafecas, Núria; Martínez Alonso, Héctor. - : ACL (Association for Computational Linguistics) BASE Show details

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