ClaiRE at SemEval-2018 Task 7 - Extended Version
This work addresses a domain-specific NLP task for researchers analyzing scientific texts, but it is incremental as it builds on prior work with minor improvements.
The paper tackles the problem of classifying semantic relations in scientific literature for clean and noisy data, achieving improved F1 scores of 75.11% for subtask 1.1 and 81.44% for subtask 1.2.
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.