@inproceedings{mccrae-arcan-2020-nuig,
title = "{NUIG} at {TIAD}: Combining Unsupervised {NLP} and Graph Metrics for Translation Inference",
author = "McCrae, John Philip and
Arcan, Mihael",
editor = "Kernerman, Ilan and
Krek, Simon and
McCrae, John P. and
Gracia, Jorge and
Ahmadi, Sina and
Kabashi, Besim",
booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.globalex-1.15/",
pages = "92--97",
language = "eng",
ISBN = "979-10-95546-46-7",
abstract = "In this paper, we present the NUIG system at the TIAD shard task. This system includes graph-based metrics calculated using novel algorithms, with an unsupervised document embedding tool called ONETA and an unsupervised multi-way neural machine translation method. The results are an improvement over our previous system and produce the highest precision among all systems in the task as well as very competitive F-Measure results. Incorporating features from other systems should be easy in the framework we describe in this paper, suggesting this could very easily be extended to an even stronger result."
}
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<abstract>In this paper, we present the NUIG system at the TIAD shard task. This system includes graph-based metrics calculated using novel algorithms, with an unsupervised document embedding tool called ONETA and an unsupervised multi-way neural machine translation method. The results are an improvement over our previous system and produce the highest precision among all systems in the task as well as very competitive F-Measure results. Incorporating features from other systems should be easy in the framework we describe in this paper, suggesting this could very easily be extended to an even stronger result.</abstract>
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%0 Conference Proceedings
%T NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference
%A McCrae, John Philip
%A Arcan, Mihael
%Y Kernerman, Ilan
%Y Krek, Simon
%Y McCrae, John P.
%Y Gracia, Jorge
%Y Ahmadi, Sina
%Y Kabashi, Besim
%S Proceedings of the 2020 Globalex Workshop on Linked Lexicography
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-46-7
%G eng
%F mccrae-arcan-2020-nuig
%X In this paper, we present the NUIG system at the TIAD shard task. This system includes graph-based metrics calculated using novel algorithms, with an unsupervised document embedding tool called ONETA and an unsupervised multi-way neural machine translation method. The results are an improvement over our previous system and produce the highest precision among all systems in the task as well as very competitive F-Measure results. Incorporating features from other systems should be easy in the framework we describe in this paper, suggesting this could very easily be extended to an even stronger result.
%U https://aclanthology.org/2020.globalex-1.15/
%P 92-97
Markdown (Informal)
[NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference](https://aclanthology.org/2020.globalex-1.15/) (McCrae & Arcan, GLOBALEX 2020)
ACL