Deep Learning Embeddings for Discontinuous Linguistic Units
This work addresses a specific challenge in natural language processing for tasks like coreference resolution, but it is incremental as it builds on existing embedding methods.
The paper tackled the problem of representing discontinuous linguistic units with deep learning embeddings, showing that these embeddings outperform word form embeddings in coreference resolution.
Deep learning embeddings have been successfully used for many natural language processing problems. Embeddings are mostly computed for word forms although a number of recent papers have extended this to other linguistic units like morphemes and phrases. In this paper, we argue that learning embeddings for discontinuous linguistic units should also be considered. In an experimental evaluation on coreference resolution, we show that such embeddings perform better than word form embeddings.