Additive Compositionality of Word Vectors
Yeon Seonwoo, Sung‐Joon Park, Dongkwan Kim, Alice H. Oh · 2019
Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives.Existing research on justifying additive compositionality of existing word embedding models requires a rather strong assumption of uniform word distribution.In this paper, we relax that assumption and propose more realistic conditions for proving additive compositionality, and we develop a novel word and sub-word embedding model that satisfies additive compositionality under those conditions.We then empirically show our model's improved semantic representation performance on word similarity and noisy sentence similarity.