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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 – 10 of 10 1 Probing for idiomaticity in vector space models Garcia, M.; Vieira, T.K.; Scarton, C.. - : Association for Computational Linguistics (ACL), 2021 BASE Show details 2 Unsupervised compositionality prediction of nominal compounds Cordeiro, S.; Villavicencio, A.; Idiart, M.. - : MIT Press - Journals, 2019 BASE Show details 3 Discovering multiword expressions Villavicencio, A.; Idiart, M.. - : Cambridge University Press (CUP), 2019 BASE Show details 4 Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks Paula, F.; Wilkens, R.; Idiart, M.. - : Association for Computational Linguistics, 2018 BASE Show details 5 How naked is the naked truth? A multilingual lexicon of nominal compound compositionality Villavicencio, A.; Wilkens, R.; Ramisch, C.. - : Association for Computational Linguistics, 2016 BASE Show details 6 Multiword expressions in child language Wilkens, R.; Idiart, M.; Villavicencio, A.. - : European Language Resources Association (ELRA), 2016 BASE Show details 7 Predicting the compositionality of nominal compounds: Giving word embeddings a hard time Cordeiro, S.; Ramisch, C.; Idiart, M.; Villavicencio, A.. - : Association for Computational Linguistics, 2016 Abstract: Distributional semantic models (DSMs) are often evaluated on artificial similarity datasets containing single words or fully compositional phrases. We present a large-scale multilingual evaluation of DSMs for predicting the degree of semantic compositionality of nominal compounds on 4 datasets for English and French. We build a total of 816 DSMs and perform 2,856 evaluations using word2vec, GloVe, and PPMI-based models. In addition to the DSMs, we compare the impact of different parameters, such as level of corpus preprocessing, context window size and number of dimensions. The results obtained have a high correlation with human judgments, being comparable to or outperforming the state of the art for some datasets (Spearman's ρ=.82 for the Reddy dataset). URL: http://eprints.whiterose.ac.uk/153562/ BASE Hide details 8 Matrix factorization using window sampling and negative sampling for improved word representations Salle, A.; Idiart, M.; Villavicencio, A.. - : Association for Computational Linguistics, 2016 BASE Show details 9 Nothing like good old frequency: Studying context filters for distributional thesauri Padró, M.; Idiart, M.; Ramisch, C.. - : Association for Computational Linguistics, 2014 BASE Show details 10 Language acquisition and probabilistic models : keeping it simple Villavicencio, A.; Malioutov, I.; Idiart, M.. - : Association for Computational Linguistics (ACL), 2013 BASE Show details

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