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• W2964091096 abstract "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 information and labels---in a tensor that captures conjunction information among these parts. We apply low-rank tensor approximations to the corresponding parameter tensors to reduce the parameter space and improve prediction speed. Furthermore, we investigate two methods for handling features that include $n$-grams of mixed lengths. Our model achieves state-of-the-art results on tasks in relation extraction, PP-attachment, and preposition disambiguation." @default.
• W2964091096 created "2019-07-30" @default.
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• W2964091096 date "2016-01-01" @default.
• W2964091096 modified "2023-10-14" @default.
• W2964091096 title "Embedding Lexical Features via Low-Rank Tensors" @default.
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• W2964091096 doi "https://doi.org/10.18653/v1/n16-1117" @default.
• W2964091096 hasPublicationYear "2016" @default.
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