Items where Author is "Paun, Silviu"

Article

Poesio, Massimo and Yu, Juntao and Paun, Silviu and Aloraini, Abdulrahman and Lu, Pengcheng and Haber, Janosch and Cokal, Derya (2023) Computational Models of Anaphora. Annual Review of Linguistics, 9 (1). pp. 561-587. DOI https://doi.org/10.1146/annurev-linguistics-031120-111653

Paun, Silviu and Carpenter, Bob and Chamberlain, JD and Hovy, Dirk and Kruschwitz, Udo and Poesio, Massimo (2018) Comparing Bayesian Models of Annotation. Transactions of the Association for Computational Linguistics, 6 (2018). pp. 571-585. DOI https://doi.org/10.1162/tacl_a_00040

Conference or Workshop Item

Yu, Juntao and Paun, Silviu and Camilleri, Maris and Carretero Garcia, Paloma and Chamberlain, Jon and Kruschwitz, Udo and Poesio, Massimo (2023) Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts. In: The 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023-05-02 - 2023-05-06, Dubrovnik, Croatia.

Madge, Chris and Yu, Juntao and Chamberlain, Jon and Kruschwitz, Udo and Paun, Silviu and Poesio, Massimo (2019) Crowdsourcing and Aggregating Nested Markable Annotations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019-07-28 - 2019-08-02.

Poesio, Massimo and Chamberlain, Jon and Paun, Silviu and Yu, Juntao and Uma, Alexandra and Kruschwitz, Udo (2019) A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019-06-02 - 2019-06-07, Minneapolis, Minnesota.

Paun, Silviu and Chamberlain, Jon and Kruschwitz, Udo and Yu, Juntao and Poesio, Massimo (2018) A Probabilistic Annotation Model for Crowdsourcing Coreference. In: Empirical Methods in Natural Language Processing, 2018-10-31 - 2018-11-04, Brussels, Belgium.

Thesis

Paun, Silviu (2017) Topic models for short text data. PhD thesis, University of Essex.