@inproceedings{yirmibesoglu-gungor-2020-ermi,
title = "{ERMI} at {PARSEME} Shared Task 2020: Embedding-Rich Multiword Expression Identification",
author = {Yirmibe{\c{s}}o{\u{g}}lu, Zeynep and
G{\"u}ng{\"o}r, Tunga},
editor = "Markantonatou, Stella and
McCrae, John and
Mitrovi{\'c}, Jelena and
Tiberius, Carole and
Ramisch, Carlos and
Vaidya, Ashwini and
Osenova, Petya and
Savary, Agata",
booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
month = dec,
year = "2020",
address = "online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.mwe-1.17",
pages = "130--135",
abstract = "This paper describes the ERMI system submitted to the closed track of the PARSEME shared task 2020 on automatic identification of verbal multiword expressions (VMWEs). ERMI is an embedding-rich bidirectional LSTM-CRF model, which takes into account the embeddings of the word, its POS tag, dependency relation, and its head word. The results are reported for 14 languages, where the system is ranked 1st in the general cross-lingual ranking of the closed track systems, according to the Unseen MWE-based F1.",
}
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<abstract>This paper describes the ERMI system submitted to the closed track of the PARSEME shared task 2020 on automatic identification of verbal multiword expressions (VMWEs). ERMI is an embedding-rich bidirectional LSTM-CRF model, which takes into account the embeddings of the word, its POS tag, dependency relation, and its head word. The results are reported for 14 languages, where the system is ranked 1st in the general cross-lingual ranking of the closed track systems, according to the Unseen MWE-based F1.</abstract>
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%0 Conference Proceedings
%T ERMI at PARSEME Shared Task 2020: Embedding-Rich Multiword Expression Identification
%A Yirmibeşoğlu, Zeynep
%A Güngör, Tunga
%Y Markantonatou, Stella
%Y McCrae, John
%Y Mitrović, Jelena
%Y Tiberius, Carole
%Y Ramisch, Carlos
%Y Vaidya, Ashwini
%Y Osenova, Petya
%Y Savary, Agata
%S Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons
%D 2020
%8 December
%I Association for Computational Linguistics
%C online
%F yirmibesoglu-gungor-2020-ermi
%X This paper describes the ERMI system submitted to the closed track of the PARSEME shared task 2020 on automatic identification of verbal multiword expressions (VMWEs). ERMI is an embedding-rich bidirectional LSTM-CRF model, which takes into account the embeddings of the word, its POS tag, dependency relation, and its head word. The results are reported for 14 languages, where the system is ranked 1st in the general cross-lingual ranking of the closed track systems, according to the Unseen MWE-based F1.
%U https://aclanthology.org/2020.mwe-1.17
%P 130-135
Markdown (Informal)
[ERMI at PARSEME Shared Task 2020: Embedding-Rich Multiword Expression Identification](https://aclanthology.org/2020.mwe-1.17) (Yirmibeşoğlu & Güngör, MWE 2020)
ACL