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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 – 20 of 20 1 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 2 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 5 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 6 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 7 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 8 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko... In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018) BASE Show details 9 Universal Dependencies 2.3 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details 10 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details 11 Universal Dependencies 2.1 Nivre, Joakim; Agić, Željko; Ahrenberg, Lars... In: https://hal.inria.fr/hal-01682188 ; 2017 (2017) BASE Show details 12 Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017 BASE Show details 13 Universal Dependencies 2.1 Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017 BASE Show details 14 CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies Çöltekin, Çağrı; Kayadelen, Tolga; Droganova, Kira. - : Association for Computational Linguistics, 2017. : country:USA, 2017. : place:Stroudsburg, PA, 2017 BASE Show details 15 Creating robust supervised classifiers via web-scale n-gram data Bergsma, Shane; Pitler, Emily; Lin, Dekang In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 2, 865-874 BLLDB Show details 16 Automatic evaluation of linguistic quality in multi-document summarization Pitler, Emily; Louis, Annie; Nenkova, Ani In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 544-554 BLLDB Show details 17 Automatic Evaluation of Linguistic Quality in Multi-Document Summarization Pitler, Emily; Louis, Annie; Nenkova, Ani In: Departmental Papers (CIS) (2010) BASE Show details 18 Structural Features for Predicting the Linguistic Quality of Text: Applications to Machine Translation, Automatic Summarization and Human-Authored Text Nenkova, Ani; Chae, Jieun; Louis, Annie... In: Departmental Papers (CIS) (2010) BASE Show details 19 Automatic Sense Prediction for Implicit Discourse Relations in Text Pitler, Emily; Louis, Annie; Nenkova, Ani In: Departmental Papers (CIS) (2009) Abstract: We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as “but” or “because”. We work with a corpus of implicit relations present in newspaper text and report results on a test set that is representative of the naturally occurring distribution of senses. We use several linguistically informed features, including polarity tags, Levin verb classes, length of verb phrases, modality, context, and lexical features. In addition, we revisit past approaches using lexical pairs from unannotated text as features, explain some of their shortcomings and propose modifications. Our best combination of features outperforms the baseline from data intensive approaches by 4% for comparison and 16% for contingency. Keyword: Computer Sciences URL: https://repository.upenn.edu/cgi/viewcontent.cgi?article=1760&context=cis_papers https://repository.upenn.edu/cis_papers/722 BASE Hide details 20 Revisiting Readability: A Unified Framework for Predicting Text Quality Nenkova, Ani; Pitler, Emily In: Departmental Papers (CIS) (2008) BASE Show details

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