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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 Page: 1 2 3 4 5 Hits 21 – 40 of 97 21 A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation Poesio, Massimo; Chamberlain, Jon; Paun, Silviu. - : Association for Computational Linguistics, 2019 BASE Show details 22 Comparing Bayesian Models of Annotation Paun, Silviu; Carpenter, Bob; Hovy, Dirk. - : Association for Computational Linguistics, 2018 BASE Show details 23 A Probabilistic Annotation Model for Crowdsourcing Coreference Kruschwitz, Udo; Chamberlain, Jon; Yu, Juntao. - : Association for Computational Linguistics, 2018 BASE Show details 24 Comparing Bayesian Models of Annotation Paun, S.; Carpenter, B.; Chamberlain, J. D.. - : Association for Computational Linguistics, 2018 BASE Show details 25 Phrase Detectives Corpus Chamberlain, Jon; Poesio, Massimo; Kruschwitz, Udo. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017 BASE Show details 26 Phrase Detectives Corpus ... Chamberlain, Jon; Poesio, Massimo; Kruschwitz, Udo. - : Linguistic Data Consortium, 2017 BASE Show details 27 Anaphora resolution : algorithms, resources, and applications Poesio, Massimo [Herausgeber]; Stuckardt, Roland [Herausgeber]; Versley, Yannick [Herausgeber]. - 2016 DNB Subject Category Language Show details 28 Anaphora Resolution : Algorithms, Resources, and Applications Poesio, Massimo [Herausgeber]; Stuckardt, Roland [Herausgeber]; Versley, Yannick [Herausgeber]. - Berlin, Heidelberg : Springer Berlin Heidelberg, 2016 DNB Subject Category Language Show details 29 Anaphora resolution : algorithms, resources, and applications Poesio, Massimo (Herausgeber); Stuckardt, Roland (Herausgeber); Versley, Yannick (Herausgeber). - Heidelberg : Springer, 2016 UB Frankfurt Linguistik Show details 30 Grammar Is a System That Characterizes Talk in Interaction Poesio, Massimo; Ginzburg, Jonathan. - : Frontiers Media SA, 2016 BASE Show details 31 The SENSEI Project: Making Sense of Human Conversations Riccardi, Giuseppe; Bechet, Frederic; Danieli, Morena. - : Springer International Publishing, 2016 BASE Show details 32 Pandora’s Box Opened Rieser, Hannes; Poesio, Massimo. - : Walter de Gruyter GmbH, 2016 BASE Show details 33 MultiLing 2015: Multilingual Summarization of Single and Multi-Documents, On-line Fora, and Call-center Conversations Giannakopoulos, George; Kubina, Jeff; Conroy, John... In: Sigdial ; https://hal-amu.archives-ouvertes.fr/hal-01194230 ; Sigdial, 2015, Unknown, Unknown Region (2015) BASE Show details 34 Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text Anderson, Andrew James; Bruni, Elia; Lopopolo, Alessandro. - : Elsevier BV, 2015 BASE Show details 35 Combining Minimally-supervised Methods for Arabic Named Entity Recognition Althobaiti, M.; Kruschwitz, Udo; Poesio, Massimo. - : Association for Computational Linguistics, 2015 Abstract: Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better results may be obtained by combining the two types of minimally supervised methods. In this paper we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We trained a semi-supervised NER classifier and another one using distant learning techniques, and then combined them using a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best base classifiers. Keyword: 020 Bibliotheks- und Informationswissenschaft; ddc:020 URL: https://epub.uni-regensburg.de/40345/ https://epub.uni-regensburg.de/40345/1/564-1673-1-PB.pdf https://transacl.org/ojs/index.php/tacl/article/view/564 BASE Hide details 36 The effect of linguistic and visual salience in visual world studies Cavicchio, Federica; Melcher, David; Poesio, Massimo. - : Frontiers Media SA, 2014 BASE Show details 37 Identifying fake Amazon reviews as learning from crowds Fornaciari, Tommaso; Poesio, Massimo. - : Association for Computational Linguistics, 2014 BASE Show details 38 Using Brain Data for Sentiment Analysis. Gu, Yuqiao; Celli, Fabio; Steinberger, Josef. - 2014 BASE Show details 39 Discriminating Taxonomic Categories and Domains in Mental Simulations of Concepts of Varying Concreteness Anderson, Andrew J; Murphy, Brian; Poesio, Massimo. - : MIT Press - Journals, 2014 BASE Show details 40 The effect of linguistic and visual salience in visual world studies Cavicchio, Federica; Melcher, David; Poesio, Massimo. - : Frontiers Media S.A., 2014 BASE Show details Page: 1 2 3 4 5

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