@inproceedings{dehouck-denis-2018-framework,
title = "A Framework for Understanding the Role of Morphology in {U}niversal {D}ependency Parsing",
author = "Dehouck, Mathieu and
Denis, Pascal",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1312",
doi = "10.18653/v1/D18-1312",
pages = "2864--2870",
abstract = "This paper presents a simple framework for characterizing morphological complexity and how it encodes syntactic information. In particular, we propose a new measure of morpho-syntactic complexity in terms of governor-dependent preferential attachment that explains parsing performance. Through experiments on dependency parsing with data from Universal Dependencies (UD), we show that representations derived from morphological attributes deliver important parsing performance improvements over standard word form embeddings when trained on the same datasets. We also show that the new morpho-syntactic complexity measure is predictive of the gains provided by using morphological attributes over plain forms on parsing scores, making it a tool to distinguish languages using morphology as a syntactic marker from others.",
}
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%0 Conference Proceedings
%T A Framework for Understanding the Role of Morphology in Universal Dependency Parsing
%A Dehouck, Mathieu
%A Denis, Pascal
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F dehouck-denis-2018-framework
%X This paper presents a simple framework for characterizing morphological complexity and how it encodes syntactic information. In particular, we propose a new measure of morpho-syntactic complexity in terms of governor-dependent preferential attachment that explains parsing performance. Through experiments on dependency parsing with data from Universal Dependencies (UD), we show that representations derived from morphological attributes deliver important parsing performance improvements over standard word form embeddings when trained on the same datasets. We also show that the new morpho-syntactic complexity measure is predictive of the gains provided by using morphological attributes over plain forms on parsing scores, making it a tool to distinguish languages using morphology as a syntactic marker from others.
%R 10.18653/v1/D18-1312
%U https://aclanthology.org/D18-1312
%U https://doi.org/10.18653/v1/D18-1312
%P 2864-2870
Markdown (Informal)
[A Framework for Understanding the Role of Morphology in Universal Dependency Parsing](https://aclanthology.org/D18-1312) (Dehouck & Denis, EMNLP 2018)
ACL