@inproceedings{krasnowska-kieras-wolinski-2024-parsing-headed,
title = "Parsing Headed Constituencies",
author = "Krasnowska-Kiera{\'s}, Katarzyna and
Woli{\'n}ski, Marcin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1106",
pages = "12633--12643",
abstract = "In the paper, we present a parsing technique that generates headed constituency trees, which combine information typically contained in constituency and dependency trees. We advocate for using such structures for syntactic representation. The parsing method combines prediction of dependency links with prediction of constituency spines in a {`}parsing as tagging{'} approach and outputs a hybrid structure. An interesting feature is that the method can generate constituency trees with discontinuities. The parser is built on top of a BERT model for the given language and uses a specially crafted classifier for predicting dependency links. With suitable training data the method can be applied to arbitrary language; we report evaluation results for Polish and German.",
}
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<abstract>In the paper, we present a parsing technique that generates headed constituency trees, which combine information typically contained in constituency and dependency trees. We advocate for using such structures for syntactic representation. The parsing method combines prediction of dependency links with prediction of constituency spines in a ‘parsing as tagging’ approach and outputs a hybrid structure. An interesting feature is that the method can generate constituency trees with discontinuities. The parser is built on top of a BERT model for the given language and uses a specially crafted classifier for predicting dependency links. With suitable training data the method can be applied to arbitrary language; we report evaluation results for Polish and German.</abstract>
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%0 Conference Proceedings
%T Parsing Headed Constituencies
%A Krasnowska-Kieraś, Katarzyna
%A Woliński, Marcin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F krasnowska-kieras-wolinski-2024-parsing-headed
%X In the paper, we present a parsing technique that generates headed constituency trees, which combine information typically contained in constituency and dependency trees. We advocate for using such structures for syntactic representation. The parsing method combines prediction of dependency links with prediction of constituency spines in a ‘parsing as tagging’ approach and outputs a hybrid structure. An interesting feature is that the method can generate constituency trees with discontinuities. The parser is built on top of a BERT model for the given language and uses a specially crafted classifier for predicting dependency links. With suitable training data the method can be applied to arbitrary language; we report evaluation results for Polish and German.
%U https://aclanthology.org/2024.lrec-main.1106
%P 12633-12643
Markdown (Informal)
[Parsing Headed Constituencies](https://aclanthology.org/2024.lrec-main.1106) (Krasnowska-Kieraś & Woliński, LREC-COLING 2024)
ACL
- Katarzyna Krasnowska-Kieraś and Marcin Woliński. 2024. Parsing Headed Constituencies. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12633–12643, Torino, Italia. ELRA and ICCL.