@inproceedings{al-ghezi-kurimo-2020-graph,
title = "Graph-based Syntactic Word Embeddings",
author = "Al-Ghezi, Ragheb and
Kurimo, Mikko",
editor = "Ustalov, Dmitry and
Somasundaran, Swapna and
Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Hulpu{\textcommabelow{s}}, Ioana and
Jansen, Peter and
Jana, Abhik",
booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.textgraphs-1.8",
doi = "10.18653/v1/2020.textgraphs-1.8",
pages = "72--78",
abstract = "We propose a simple and efficient framework to learn syntactic embeddings based on information derived from constituency parse trees. Using biased random walk methods, our embeddings not only encode syntactic information about words, but they also capture contextual information. We also propose a method to train the embeddings on multiple constituency parse trees to ensure the encoding of global syntactic representation. Quantitative evaluation of the embeddings show a competitive performance on POS tagging task when compared to other types of embeddings, and qualitative evaluation reveals interesting facts about the syntactic typology learned by these embeddings.",
}
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<abstract>We propose a simple and efficient framework to learn syntactic embeddings based on information derived from constituency parse trees. Using biased random walk methods, our embeddings not only encode syntactic information about words, but they also capture contextual information. We also propose a method to train the embeddings on multiple constituency parse trees to ensure the encoding of global syntactic representation. Quantitative evaluation of the embeddings show a competitive performance on POS tagging task when compared to other types of embeddings, and qualitative evaluation reveals interesting facts about the syntactic typology learned by these embeddings.</abstract>
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%0 Conference Proceedings
%T Graph-based Syntactic Word Embeddings
%A Al-Ghezi, Ragheb
%A Kurimo, Mikko
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Hulpu\textcommabelows, Ioana
%Y Jansen, Peter
%Y Jana, Abhik
%S Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F al-ghezi-kurimo-2020-graph
%X We propose a simple and efficient framework to learn syntactic embeddings based on information derived from constituency parse trees. Using biased random walk methods, our embeddings not only encode syntactic information about words, but they also capture contextual information. We also propose a method to train the embeddings on multiple constituency parse trees to ensure the encoding of global syntactic representation. Quantitative evaluation of the embeddings show a competitive performance on POS tagging task when compared to other types of embeddings, and qualitative evaluation reveals interesting facts about the syntactic typology learned by these embeddings.
%R 10.18653/v1/2020.textgraphs-1.8
%U https://aclanthology.org/2020.textgraphs-1.8
%U https://doi.org/10.18653/v1/2020.textgraphs-1.8
%P 72-78
Markdown (Informal)
[Graph-based Syntactic Word Embeddings](https://aclanthology.org/2020.textgraphs-1.8) (Al-Ghezi & Kurimo, TextGraphs 2020)
ACL
- Ragheb Al-Ghezi and Mikko Kurimo. 2020. Graph-based Syntactic Word Embeddings. In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs), pages 72–78, Barcelona, Spain (Online). Association for Computational Linguistics.