Word-Level Maximum Mean Discrepancy Regularization for Word Embedding
用于词嵌入的词级最大均值差异正则化
The technique of word embedding is widely used in natural language processing (NLP) to represent words as numerical vectors in textual datasets. However, the estimation of word embedding may suffer from severe overfitting due to the huge variety of words. To address the issue, this article proposes a novel regularization framework that recognizes and accounts for the “word-level distribution discrepancy”–a common phenomenon in a range of NLP tasks where word distributions…