@inproceedings{steinbach-rehbein-2019-automatic,
title = "Automatic Alignment and Annotation Projection for Literary Texts",
author = "Steinbach, Uli and
Rehbein, Ines",
editor = "Alex, Beatrice and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = jun,
year = "2019",
address = "Minneapolis, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2505",
doi = "10.18653/v1/W19-2505",
pages = "35--45",
abstract = "This paper presents a modular NLP pipeline for the creation of a parallel literature corpus, followed by annotation transfer from the source to the target language. The test case we use to evaluate our pipeline is the automatic transfer of quote and speaker mention annotations from English to German. We evaluate the different components of the pipeline and discuss challenges specific to literary texts. Our experiments show that after applying a reasonable amount of semi-automatic postprocessing we can obtain high-quality aligned and annotated resources for a new language.",
}
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%0 Conference Proceedings
%T Automatic Alignment and Annotation Projection for Literary Texts
%A Steinbach, Uli
%A Rehbein, Ines
%Y Alex, Beatrice
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F steinbach-rehbein-2019-automatic
%X This paper presents a modular NLP pipeline for the creation of a parallel literature corpus, followed by annotation transfer from the source to the target language. The test case we use to evaluate our pipeline is the automatic transfer of quote and speaker mention annotations from English to German. We evaluate the different components of the pipeline and discuss challenges specific to literary texts. Our experiments show that after applying a reasonable amount of semi-automatic postprocessing we can obtain high-quality aligned and annotated resources for a new language.
%R 10.18653/v1/W19-2505
%U https://aclanthology.org/W19-2505
%U https://doi.org/10.18653/v1/W19-2505
%P 35-45
Markdown (Informal)
[Automatic Alignment and Annotation Projection for Literary Texts](https://aclanthology.org/W19-2505) (Steinbach & Rehbein, LaTeCH 2019)
ACL
- Uli Steinbach and Ines Rehbein. 2019. Automatic Alignment and Annotation Projection for Literary Texts. In Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 35–45, Minneapolis, USA. Association for Computational Linguistics.