Supervised Phrase-boundary Embeddings
This work addresses the need for better phrase-aware embeddings in natural language processing, but it appears incremental as it modifies existing methods rather than introducing a new paradigm.
The paper tackled the problem of improving word embeddings by incorporating supervised phrase information, resulting in superior embeddings for intrinsic and downstream tasks.
We propose a new word embedding model, called SPhrase, that incorporates supervised phrase information. Our method modifies traditional word embeddings by ensuring that all target words in a phrase have exactly the same context. We demonstrate that including this information within a context window produces superior embeddings for both intrinsic evaluation tasks and downstream extrinsic tasks.