@inproceedings{hettinger-etal-2018-claire,
title = "{C}lai{RE} at {S}em{E}val-2018 Task 7: Classification of Relations using Embeddings",
author = "Hettinger, Lena and
Dallmann, Alexander and
Zehe, Albin and
Niebler, Thomas and
Hotho, Andreas",
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/S18-1134",
doi = "10.18653/v1/S18-1134",
pages = "836--841",
abstract = "In this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). We compare two models for classification, a C-LSTM which utilizes only word embeddings and an SVM that also takes handcrafted features into account. To adapt to the domain of science we train word embeddings on scientific papers collected from arXiv.org. The hand-crafted features consist of lexical features to model the semantic relations as well as the entities between which the relation holds. Classification of Relations using Embeddings (ClaiRE) achieved an F1 score of 74.89{\%} for the first subtask and 78.39{\%} for the second.",
}
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%0 Conference Proceedings
%T ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings
%A Hettinger, Lena
%A Dallmann, Alexander
%A Zehe, Albin
%A Niebler, Thomas
%A Hotho, Andreas
%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 hettinger-etal-2018-claire
%X In this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). We compare two models for classification, a C-LSTM which utilizes only word embeddings and an SVM that also takes handcrafted features into account. To adapt to the domain of science we train word embeddings on scientific papers collected from arXiv.org. The hand-crafted features consist of lexical features to model the semantic relations as well as the entities between which the relation holds. Classification of Relations using Embeddings (ClaiRE) achieved an F1 score of 74.89% for the first subtask and 78.39% for the second.
%R 10.18653/v1/S18-1134
%U https://aclanthology.org/S18-1134
%U https://doi.org/10.18653/v1/S18-1134
%P 836-841
Markdown (Informal)
[ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings](https://aclanthology.org/S18-1134) (Hettinger et al., SemEval 2018)
ACL