Deep Learning Embeddings for Discontinuous Linguistic Units
Computation and Language
2013-12-20 v2
Abstract
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.
Cite
@article{arxiv.1312.5129,
title = {Deep Learning Embeddings for Discontinuous Linguistic Units},
author = {Wenpeng Yin and Hinrich Schütze},
journal= {arXiv preprint arXiv:1312.5129},
year = {2013}
}