We present a syntactically enriched vector model that supports the computation of contextualized semantic representations in a quasi compositional fashion. It employs a systematic combination of first-and second-order context vectors. We apply our model to two different tasks and show that (i) it substantially outperforms previous work on a paraphrase ranking task, and (ii) achieves promising results on a wordsense similarity task; to our knowledge, it is the first time that an unsupervised method has been applied to this task.
A quasi-compositional approach was also attempted in REF by a systematic combination of first and second order context vectors.
Contextualizing Semantic Representations Using Syntactically Enriched Vector Models
ACL
Computer Science