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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 – 6 of 6 1 Unsupervised compositionality prediction of nominal compounds Cordeiro, S.; Villavicencio, A.; Idiart, M.. - : MIT Press - Journals, 2019 BASE Show details 2 A corpus study of verbal multiword expressions in Brazilian Portuguese Ramisch, C.; Ramisch, R.; Zilio, L.. - : Springer International Publishing, 2018 BASE Show details 3 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 4 Filtering and measuring the intrinsic quality of human compositionality judgments Ramisch, C.; Cordeiro, S.; Villavicencio, A.. - : Association for Computational Linguistics, 2016 BASE Show details 5 mwetoolkit+sem: Integrating word embeddings in the mwetoolkit for semantic MWE processing Cordeiro, S.; Ramisch, C.; Villavicencio, A.. - : European Language Resources Association (ELRA), 2016 BASE Show details 6 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

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