@inproceedings{ozates-etal-2018-morphology,
title = "A Morphology-Based Representation Model for {LSTM}-Based Dependency Parsing of Agglutinative Languages",
author = {{\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l and
{\"O}zg{\"u}r, Arzucan and
G{\"u}ng{\"o}r, Tunga and
{\"O}zt{\"u}rk, Balk{\i}z},
editor = "Zeman, Daniel and
Haji{\v{c}}, Jan",
booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K18-2024",
doi = "10.18653/v1/K18-2024",
pages = "238--247",
abstract = "We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.",
}
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<abstract>We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.</abstract>
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%0 Conference Proceedings
%T A Morphology-Based Representation Model for LSTM-Based Dependency Parsing of Agglutinative Languages
%A Özateş, Şaziye Betül
%A Özgür, Arzucan
%A Güngör, Tunga
%A Öztürk, Balkız
%Y Zeman, Daniel
%Y Hajič, Jan
%S Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ozates-etal-2018-morphology
%X We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.
%R 10.18653/v1/K18-2024
%U https://aclanthology.org/K18-2024
%U https://doi.org/10.18653/v1/K18-2024
%P 238-247
Markdown (Informal)
[A Morphology-Based Representation Model for LSTM-Based Dependency Parsing of Agglutinative Languages](https://aclanthology.org/K18-2024) (Özateş et al., CoNLL 2018)
ACL