Embedding Lexical Features via Low-Rank Tensors
HLT-NAACL, pp. 1019-1029, 2016.
Modern NLP models rely heavily on engineered features, which often combine word and contextual information into complex lexical features. Such combination results in large numbers of features, which can lead to over-fitting. We present a new model that represents complex lexical features---comprised of parts for words, contextual informat...More
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