A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data
Table 1: Performance of supervised learning models with different features.
['Feature', 'LR P', 'LR R', 'LR F1', 'SVM P', 'SVM R', 'SVM F1', 'ANN P', 'ANN R', 'ANN F1']
Word embeddings derived from Wiki-PubMed-PMC outperform GloVe-based embeddings (Table 1). The models using BoC outperform models using BoW as well as ASM features. [CONTINUE] Wikipedia-PubMed-PMC embeddings (Moen and Ananiadou, 2013) outperforms GloVe (Mikolov et al., 2013a) in the extraction of most relation types (Table 1) [CONTINUE] the combination feature of BoC and sentence embeddings outperforms sentence embeddings alone, but do not exceed the upper boundary of BoC feature, in which again demonstrating the competitiveness of BoC feature. [CONTINUE] Lin-SVM outperforms other classifiers in extracting most relations. The feed-forward ANN displays significant over-fitting across all relation types, as the performance decreases when increasing the training epochs.