A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data
Table 2: F1 score results per relation type of the best performing models.
['Relation type', 'Count', 'Intra-sentential co-occ.  [ITALIC] ρ=0', 'Intra-sentential co-occ.  [ITALIC] ρ=5', 'Intra-sentential co-occ.  [ITALIC] ρ=10', 'BoC(Wiki-PubMed-PMC) LR', 'BoC(Wiki-PubMed-PMC) SVM', 'BoC(Wiki-PubMed-PMC) ANN']
intra-sentential cooccurrence baseline outperforms other approaches which allow boundary expansion. [CONTINUE] As the results of applying the co-occurrence baseline (ρ = 0) shows (Table 2), the semantic relations in this data are strongly concentrated within a sentence boundary, especially for the relation of RecurLink, with an F1 of 1.0. The machine learning approaches based on BoC lexical features effectively complement the deficiency of cross-sentence relation extraction.