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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; Campolungo, Niccolò; Navigli, Roberto; Pasi, Gabriella; Pasini, Tommaso. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.79/ Abstract: With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably. However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets. In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information. At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query. Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, ... Keyword: Data Management System; Machine translation; Natural Language Processing; Neural Network URL: https://underline.io/lecture/37802-ir-like-a-sir-sense-enhanced-information-retrieval-for-multiple-languages https://dx.doi.org/10.48448/z11a-gr02 BASE Hide 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. - : Underline Science Inc., 2021 BASE Show 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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