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Domain Adapted Word Embeddings for Improved Sentiment Classification
2018
Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest. This paper proposes a method to combine the breadth of generic embeddings with the specificity of domain specific embeddings. The resulting embeddings, called Domain Adapted (DA) word embeddings, are formed by first aligning corresponding word vectors using Canonical Correlation Analysis (CCA) or the related nonlinear Kernel CCA (KCCA) and
doi:10.18653/v1/w18-3407
dblp:conf/acl-deeplo/SarmaLS18
fatcat:67zr6hb2nbekhfulujkzyffmda