ClaiRE at SemEval-2018 Task 7 - Extended Version
Lena Hettinger, Alexander Dallmann, Albin Zehe, Thomas Niebler,, Andreas Hotho

TL;DR
This paper presents the extended results of ClaiRE, a system for classifying semantic relations in scientific literature, achieving improved F1 scores on clean and noisy datasets in SemEval-2018 Task 7.
Contribution
The paper introduces technical enhancements and post-evaluation improvements to the ClaiRE system for semantic relation classification in scientific texts.
Findings
F1 score of 75.11% on clean data
F1 score of 81.44% on noisy data
Enhanced preprocessing improved classification performance
Abstract
In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on clas- sification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). This is an extended ver- sion of our workshop paper (Hettinger et al., 2018) including further technical details (Sec- tions 3.2 and 4.3) and changes made to the preprocessing step in the post-evaluation phase (Section 2.1). Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Biomedical Text Mining and Ontologies
