Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning

Understand

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research.

• We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence.
• Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context.
• Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process.