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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 – 7 of 7 1 Integrating Personalized PageRank into Neural Word Sense Disambiguation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bevilacqua, Michele; ElSheikh, Ahmed. - : Underline Science Inc., 2021 BASE Show details 2 {W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER} ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Campolungo, Niccolò; Cecconi, Francesco. - : Underline Science Inc., 2021 BASE Show details 3 {UniteD-SRL}: {A} Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual {S}emantic {R}ole {L}abeling ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Conia, Simone; Conia, Simone. - : Underline Science Inc., 2021 BASE Show details 4 IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Banerjee, Somnath; Blloshmi, Rexhina. - : Underline Science Inc., 2021 BASE Show details 5 ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Barba, Edoardo; Navigli, Roberto. - : Underline Science Inc., 2021 BASE Show details 6 AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Brignone, Fabrizio; Cecconi, Francesco; Conia, Simone; Navigli, Roberto; Orlando, Riccardo. - : Underline Science Inc., 2021 Abstract: Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models. Unfortunately, such systems are still not available as ready-to-use end-to-end packages, making it difficult for researchers to take advantage of their performance. The only alternative for a user interested in applying WSD to downstream tasks is to use currently available end-to-end WSD systems, which, however, still rely on graph-based heuristics or non-neural machine learning algorithms. In this paper, we fill this gap and propose AMuSE-WSD, the first end-to-end system to offer high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. We hope that AMuSE-WSD will provide a stepping stone for the integration of meaning into real-world applications and encourage further studies in lexical semantics. AMuSE-WSD is ... Keyword: Natural Language Processing; Text Generation; Topic Modelling URL: https://dx.doi.org/10.48448/ntm4-jt31 https://underline.io/lecture/39013-amuse-wsd-an-all-in-one-multilingual-system-for-easy-word-sense-disambiguation BASE Hide details 7 GeneSis: A Generative Approach to Substitutes in Context ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Lacerra, Caterina; Navigli, Roberto. - : Underline Science Inc., 2021 BASE Show details

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