@inproceedings{barik-etal-2018-ntnu,
title = "{NTNU} at {S}em{E}val-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers",
author = {Barik, Biswanath and
Sikdar, Utpal Kumar and
Gamb{\"a}ck, Bj{\"o}rn},
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1138",
doi = "10.18653/v1/S18-1138",
pages = "858--862",
abstract = "The paper presents NTNU{'}s contribution to SemEval-2018 Task 7 on relation identification and classification. The class weights and parameters of five alternative supervised classifiers were optimized through grid search and cross-validation. The outputs of the classifiers were combined through voting for the final prediction. A wide variety of features were explored, with the most informative identified by feature selection. The best setting achieved F1 scores of 47.4{\%} and 66.0{\%} in the relation classification subtasks 1.1 and 1.2. For relation identification and classification in subtask 2, it achieved F1 scores of 33.9{\%} and 17.0{\%},",
}
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%0 Conference Proceedings
%T NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers
%A Barik, Biswanath
%A Sikdar, Utpal Kumar
%A Gambäck, Björn
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F barik-etal-2018-ntnu
%X The paper presents NTNU’s contribution to SemEval-2018 Task 7 on relation identification and classification. The class weights and parameters of five alternative supervised classifiers were optimized through grid search and cross-validation. The outputs of the classifiers were combined through voting for the final prediction. A wide variety of features were explored, with the most informative identified by feature selection. The best setting achieved F1 scores of 47.4% and 66.0% in the relation classification subtasks 1.1 and 1.2. For relation identification and classification in subtask 2, it achieved F1 scores of 33.9% and 17.0%,
%R 10.18653/v1/S18-1138
%U https://aclanthology.org/S18-1138
%U https://doi.org/10.18653/v1/S18-1138
%P 858-862
Markdown (Informal)
[NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers](https://aclanthology.org/S18-1138) (Barik et al., SemEval 2018)
ACL