Learning Semantically and Additively CompositionalDistributional Representations
Table 3: Spearman’s ρ on phrase similarity
['[EMPTY]', 'AN', 'NN', 'VO', 'SVO', 'GS11', 'GS12']
The weakness of “no inverse” suggests that relaxing the constraint of inverse matrices may hurt compositionaly, though our preliminary examination on word similarities did not find any difference. The GS11 dataset appears to favor models that can learn from interactions between the subject and object arguments, such as the non-linear model Waddnl in \newcitehashimoto-EtAl:2014:EMNLP2014 and the entanglement model in \newcitekartsadrqpl2014. However, these models do not show particular advantages on other datasets. The recursive autoencoder (RAE) proposed in \newcitesocher11 shares an aspect with vecDCS as to construct meanings from parse trees. It is tested by \newciteblacoe-lapata:2012:EMNLP-CoNLL for compositionality, where vecDCS appears to be better. Nevertheless, we note that “no matrix” performs as good as vecDCS, suggesting that meaning changes caused by syntactic-semantic roles might not be major factors in these datasets, because the syntactic-semantic relations are all fixed in each dataset.