@inproceedings{mandravickaite-krilavicius-2017-identification,
title = "Identification of Multiword Expressions for {L}atvian and {L}ithuanian: Hybrid Approach",
author = "Mandravickait{\.e}, Justina and
Krilavi{\v{c}}ius, Tomas",
editor = "Markantonatou, Stella and
Ramisch, Carlos and
Savary, Agata and
Vincze, Veronika",
booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1712",
doi = "10.18653/v1/W17-1712",
pages = "97--101",
abstract = "We discuss an experiment on automatic identification of bi-gram multi-word expressions in parallel Latvian and Lithuanian corpora. Raw corpora, lexical association measures (LAMs) and supervised machine learning (ML) are used due to deficit and quality of lexical resources (e.g., POS-tagger, parser) and tools. While combining LAMs with ML is rather effective for other languages, it has shown some nice results for Lithuanian and Latvian as well. Combining LAMs with ML we have achieved 92,4{\%} precision and 52,2{\%} recall for Latvian and 95,1{\%} precision and 77,8{\%} recall for Lithuanian.",
}
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%0 Conference Proceedings
%T Identification of Multiword Expressions for Latvian and Lithuanian: Hybrid Approach
%A Mandravickaitė, Justina
%A Krilavičius, Tomas
%Y Markantonatou, Stella
%Y Ramisch, Carlos
%Y Savary, Agata
%Y Vincze, Veronika
%S Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F mandravickaite-krilavicius-2017-identification
%X We discuss an experiment on automatic identification of bi-gram multi-word expressions in parallel Latvian and Lithuanian corpora. Raw corpora, lexical association measures (LAMs) and supervised machine learning (ML) are used due to deficit and quality of lexical resources (e.g., POS-tagger, parser) and tools. While combining LAMs with ML is rather effective for other languages, it has shown some nice results for Lithuanian and Latvian as well. Combining LAMs with ML we have achieved 92,4% precision and 52,2% recall for Latvian and 95,1% precision and 77,8% recall for Lithuanian.
%R 10.18653/v1/W17-1712
%U https://aclanthology.org/W17-1712
%U https://doi.org/10.18653/v1/W17-1712
%P 97-101
Markdown (Informal)
[Identification of Multiword Expressions for Latvian and Lithuanian: Hybrid Approach](https://aclanthology.org/W17-1712) (Mandravickaitė & Krilavičius, MWE 2017)
ACL