Abstract
Table .2: F1-scores per TLINK type with different feature vectors. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors.
['[BOLD] TLINK type', '(→ [ITALIC] w1⊕→ [ITALIC] w2)', '(→ [ITALIC] w1+→ [ITALIC] w2)', '(→ [ITALIC] w1−→ [ITALIC] w2)', '→ [ITALIC] f', '((→ [ITALIC] w1⊕→ [ITALIC] w2)⊕→ [ITALIC] f)', '((→ [ITALIC] w1+→ [ITALIC] w2)⊕→ [ITALIC] f)', '((→ [ITALIC] w1−→ [ITALIC] w2)⊕→ [ITALIC] f)']
g Pairs of word vectors (→w1,→w2) are retrieved from Word2Vec pre-trained vectors. (→w1−→w2) is shown to be the best in identifying IDENTITY, BEGINS, BEGUN_BY and ENDS relation types, while the rest are best identified by (→w1⊕→w2). Combining (→w1⊕→w2) and →f improves the identification of all TLINK types in general, particularly BEGINS/BEGUN_BY and ENDS/ENDED_BY types, which were barely identified when (→w1⊕→w2) or →f is used individually as features.