@inproceedings{maldonado-etal-2017-detection,
title = "Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking",
author = "Maldonado, Alfredo and
Han, Lifeng and
Moreau, Erwan and
Alsulaimani, Ashjan and
Chowdhury, Koel Dutta and
Vogel, Carl and
Liu, Qun",
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/W17-1715",
doi = "10.18653/v1/W17-1715",
pages = "114--120",
abstract = "A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented. The system mainly exploits universal syntactic dependency features through a Conditional Random Fields (CRF) sequence model. The system competed in the Closed Track at the PARSEME VMWE Shared Task 2017, ranking 2nd place in most languages on full VMWE-based evaluation and 1st in three languages on token-based evaluation. In addition, this paper presents an option to re-rank the 10 best CRF-predicted sequences via semantic vectors, boosting its scores above other systems in the competition. We also show that all systems in the competition would struggle to beat a simple lookup baseline system and argue for a more purpose-specific evaluation scheme.",
}
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%0 Conference Proceedings
%T Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking
%A Maldonado, Alfredo
%A Han, Lifeng
%A Moreau, Erwan
%A Alsulaimani, Ashjan
%A Chowdhury, Koel Dutta
%A Vogel, Carl
%A Liu, Qun
%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 maldonado-etal-2017-detection
%X A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented. The system mainly exploits universal syntactic dependency features through a Conditional Random Fields (CRF) sequence model. The system competed in the Closed Track at the PARSEME VMWE Shared Task 2017, ranking 2nd place in most languages on full VMWE-based evaluation and 1st in three languages on token-based evaluation. In addition, this paper presents an option to re-rank the 10 best CRF-predicted sequences via semantic vectors, boosting its scores above other systems in the competition. We also show that all systems in the competition would struggle to beat a simple lookup baseline system and argue for a more purpose-specific evaluation scheme.
%R 10.18653/v1/W17-1715
%U https://aclanthology.org/W17-1715
%U https://doi.org/10.18653/v1/W17-1715
%P 114-120
Markdown (Informal)
[Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking](https://aclanthology.org/W17-1715) (Maldonado et al., MWE 2017)
ACL