A Unified Taxonomy of Deep Syntactic Relations

no code implementations • 21 Mar 2023 • Kira Droganova, Daniel Zeman

This paper analyzes multiple deep-syntactic frameworks with the goal of creating a proposal for a set of universal semantic role labels.

\'UFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing

no code implementations • CONLL 2019 • Kira Droganova, Andrey Kutuzov, Nikita Mediankin, Daniel Zeman

This paper describes the {\'U}FAL--Oslo system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP, Oepen et al. 2019).

Semantic Parsing

AGRR 2019: Corpus for Gapping Resolution in Russian

no code implementations • WS 2019 • Maria Ponomareva, Kira Droganova, Ivan Smurov, Tatiana Shavrina

This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7. 5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts.

Towards Deep Universal Dependencies

no code implementations • WS 2019 • Kira Droganova, Daniel Zeman

AGRR-2019: A Corpus for Gapping Resolution in Russian

no code implementations • 10 Jun 2019 • Maria Ponomareva, Kira Droganova, Ivan Smurov, Tatiana Shavrina

This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7. 5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts.

Mind the Gap: Data Enrichment in Dependency Parsing of Elliptical Constructions

no code implementations • WS 2018 • Kira Droganova, Filip Ginter, Jenna Kanerva, Daniel Zeman

In this paper, we focus on parsing rare and non-trivial constructions, in particular ellipsis.

Dependency Parsing

Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions

no code implementations • LREC 2018 • Kira Droganova, Daniel Zeman, Jenna Kanerva, Filip Ginter

CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

no code implementations • CONLL 2017 • Daniel Zeman, Martin Popel, Milan Straka, Jan Haji{\v{c}}, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinkov{\'a}, Jan Haji{\v{c}} jr., Jaroslava Hlav{\'a}{\v{c}}ov{\'a}, V{\'a}clava Kettnerov{\'a}, Zde{\v{n}}ka Ure{\v{s}}ov{\'a}, Jenna Kanerva, Stina Ojala, Anna Missil{\"a}, Christopher D. Manning, Sebastian Schuster, Siva Reddy, Dima Taji, Nizar Habash, Herman Leung, Marie-Catherine de Marneffe, Manuela Sanguinetti, Maria Simi, Hiroshi Kanayama, Valeria de Paiva, Kira Droganova, H{\'e}ctor Mart{\'\i}nez Alonso, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Umut Sulubacak, Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Georg Rehm, Tolga Kayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, M, Michael l, Jesse Kirchner, Hector Fern Alcalde, ez, Jana Strnadov{\'a}, Esha Banerjee, Ruli Manurung, Antonio Stella, Atsuko Shimada, Sookyoung Kwak, Gustavo Mendon{\c{c}}a, L, Tatiana o, Rattima Nitisaroj, Josie Li

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.

Dependency Parsing

Elliptic Constructions: Spotting Patterns in UD Treebanks

no code implementations • WS 2017 • Kira Droganova, Daniel Zeman