ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings

Lena Hettinger, Alexander Dallmann, Albin Zehe, Thomas Niebler, Andreas Hotho


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.
Anthology ID:
S18-1134
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
836–841
Language:
URL:
https://www.aclweb.org/anthology/S18-1134
DOI:
10.18653/v1/S18-1134
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1134.pdf