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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 The LAMBADA dataset ... Paperno, Denis; Kruszewski, Germán; Lazaridou, Angeliki. - : Zenodo, 2016 BASE Show details 2 The LAMBADA dataset ... Paperno, Denis; Kruszewski, Germán; Lazaridou, Angeliki. - : Zenodo, 2016 BASE Show details 3 The LAMBADA dataset: Word prediction requiring a broad discourse context ... Paperno, Denis; Kruszewski, Germán; Lazaridou, Angeliki. - : arXiv, 2016 BASE Show details 4 Distributional semantics in use Boleda, Gemma; Bernardi, Raffaella; Fernández, Raquel. - : ACL (Association for Computational Linguistics) BASE Show details 5 The LAMBADA dataset: word prediction requiring a broad discourse context Kruszewski, German; Fernandez, Raquel; Baroni, Marco. - : ACL (Association for Computational Linguistics) BASE Show details 6 Short-term meaning shift: a distributional exploration Del Tredici, Marco; Boleda, Gemma; Fernández, Raquel. - : ACL (Association for Computational Linguistics) Abstract: Comunicació presentada a la Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019), celebrada els dies 2 a 7 de juny de 2019 a Minneapolis, Estats Units d'Amèrica. ; We present the first exploration of meaning shift over short periods of time in online communities using distributional representations. We create a small annotated dataset and use it to assess the performance of a standard model for meaning shift detection on shortterm meaning shift. We find that the model has problems distinguishing meaning shift from referential phenomena, and propose a measure of contextual variability to remedy this. ; The research carried out by the Amsterdam section of the team was partially funded by the Netherlands Organisation for Scientific Research (NWO) under VIDI grant no. 276-89-008, Asymmetry in Conversation. This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 715154), and from the Spanish Ramón y Cajal programme (grant RYC-2015-18907). Keyword: Computational linguistics; Computational semantics; Deep learning; Distributional semantics; Meaning shift; Online communities; Semantic change; Short-time meaning shift URL: http://hdl.handle.net/10230/42449 BASE Hide details

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