Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers

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Learning Word Representations with Hierarchical Sparse Coding (2014, methodology): “The key novelty in our method is to govern the relationships among dimensions of the learned word vectors, introducing a hierarchical organization imposed through a structured penalty known as the group lasso (Yuan & Lin, 2006).”
Linguistic Structured Sparsity in Text Categorization (2014, methodology): “The parse-tree regularizer (omitting the group 5Formally, this is equivalent to including one additional group for each word type. coefficients and λ) for one sentence with the parse tree shown in Figure 1 is: Ωtree(w) =p |wthe |2 + |wactors |2 + |ware |2 + |wfantastic |2 + |w.|2 + p |ware |2 +…”
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification (2016, methodology): “Inspired by prior work exploiting regularization to encode structure for NLP tasks (Yogatama and Smith, 2014; Wal-lace et al., 2015), we impose different regularization penalties on weights for features generated from the respective word embedding sets.”