Abstract
Table .3: Classifier performance per TLINK type with different feature vectors, evaluated on TempEval-3-platinum. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors.
['[BOLD] TLINK type', '(→ [ITALIC] w1⊕→ [ITALIC] w2)  [BOLD] P', '(→ [ITALIC] w1⊕→ [ITALIC] w2)  [BOLD] R', '(→ [ITALIC] w1⊕→ [ITALIC] w2)  [BOLD] F1', '→ [ITALIC] f  [BOLD] P', '→ [ITALIC] f  [BOLD] R', '→ [ITALIC] f  [BOLD] F1', '((→ [ITALIC] w1⊕→ [ITALIC] w2)⊕→ [ITALIC] f)  [BOLD] P', '((→ [ITALIC] w1⊕→ [ITALIC] w2)⊕→ [ITALIC] f)  [BOLD] R', '((→ [ITALIC] w1⊕→ [ITALIC] w2)⊕→ [ITALIC] f)  [BOLD] F1']
In general, using only (→w1⊕→w2) as features does not give any benefit since the performance is significantly worse compared to using only traditional features →f, i.e. 0.4271 vs 0.5043 F1-scores. Combining the word embedding and traditional features ((→w1⊕→w2)⊕→f) also does not improve the classifier performance in general. However, if we look into each TLINK type, the classifier performance in identifying IDENTITY, SIMULTANEOUS and IS_INCLUDED is improved, and quite significantly for SIMULTANEOUS.