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Deep Learning Embeddings for Discontinuous Linguistic Units
Wenpeng Yin, Hinrich Schütze
Dec 19, 2013 (modified: Dec 19, 2013)ICLR 2014 workshop submissionreaders: everyone
Decision:submitted, no decision
Abstract:Deep learning embeddings have been successfully used for many natural language processing (NLP) 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.
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