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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 Hits 1 – 20 of 27 1 Interactive query expansion for professional search applications ... Russell-Rose, Tony; Gooch, Philip; Kruschwitz, Udo. - : arXiv, 2021 BASE Show details 2 ur-iw-hnt at GermEval 2021: An Ensembling Strategy with Multiple BERT Models ... Tran, Hoai Nam; Kruschwitz, Udo. - : arXiv, 2021 BASE Show details 3 University of Regensburg @ SwissText 2021 SEPP-NLG: Adding Sentence Structure to Unpunctuated Text Donabauer, Gregor; Kruschwitz, Udo. - : RWTH Aachen, 2021 BASE Show details 4 Phrase Detectives Corpus Version 2 Chamberlain, Jon; Paun, Silviu; Yu, Juntao. - : Linguistic Data Consortium, 2019. : https://www.ldc.upenn.edu, 2019 BASE Show details 5 Phrase Detectives Corpus Version 2 ... Chamberlain, Jon; Paun, Silviu; Yu, Juntao. - : Linguistic Data Consortium, 2019 BASE Show details 6 Crowdsourcing and Aggregating Nested Markable Annotations ... Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Universität Regensburg, 2019 BASE Show details 7 Crowdsourcing and Aggregating Nested Markable Annotations Madge, Chris; Yu, Juntao; Chamberlain, Jon; Kruschwitz, Udo; Paun, Silviu; Poesio, Massimo. - : Association for Computational Linguistics, 2019 Abstract: One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation. Markable identification is typically carried out semi-automatically, by running a markable identifier and correcting its output by hand–which is increasingly done via annotators recruited through crowdsourcing and aggregating their responses. In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector. One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task and language-independent and are thus applicable to a variety of other annotation scenarios. URL: http://repository.essex.ac.uk/25793/ http://repository.essex.ac.uk/25793/1/Madge2019Crowdsourcing.pdf https://doi.org/10.18653/v1/P19-1077 BASE Hide details 8 A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation Paun, Silviu; Uma, Alexandra; Poesio, Massimo. - : Association for Computational Linguistics, 2019 BASE Show details 9 Crowdsourcing and Aggregating Nested Markable Annotations Poesio, Massimo; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019 BASE Show details 10 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 11 Exploring Language Style in Chatbots to Increase Perceived Product Value and User Engagement Elsholz, Ela; Chamberlain, Jon; Kruschwitz, Udo. - : ACM (Association for Computing Machinery), 2019 BASE Show details 12 Comparing Bayesian Models of Annotation Paun, Silviu; Carpenter, Bob; Hovy, Dirk. - : Association for Computational Linguistics, 2018 BASE Show details 13 A Probabilistic Annotation Model for Crowdsourcing Coreference Kruschwitz, Udo; Chamberlain, Jon; Yu, Juntao. - : Association for Computational Linguistics, 2018 BASE Show details 14 Comparing Bayesian Models of Annotation Paun, S.; Carpenter, B.; Chamberlain, J. D.. - : Association for Computational Linguistics, 2018 BASE Show details 15 Phrase Detectives Corpus Chamberlain, Jon; Poesio, Massimo; Kruschwitz, Udo. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017 BASE Show details 16 Phrase Detectives Corpus ... Chamberlain, Jon; Poesio, Massimo; Kruschwitz, Udo. - : Linguistic Data Consortium, 2017 BASE Show details 17 The SENSEI Project: Making Sense of Human Conversations Riccardi, Giuseppe; Bechet, Frederic; Danieli, Morena. - : Springer International Publishing, 2016 BASE Show details 18 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 19 Creating language resources for under-resourced languages:methodologies, and experiments with Arabic El-Haj, Mahmoud; Kruschwitz, Udo; Fox, Chris. - 2015 BASE Show details 20 Creating language resources for under-resourced languages: methodologies, and experiments with Arabic El-Haj, Mahmoud; Kruschwitz, Udo; Fox, Chris. - : Springer, 2015 BASE Show details Page: 1 2

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