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cs.CL2022
Domain-Specific Word Embeddings with Structure Prediction
Stephanie Brandl, David Lassner, Anne Baillot +1
Complementary to finding good general word embeddings, an important question for representation learning is to find dynamic word embeddings, e.g., across time or domain. Current me…
cs.CL2020
Balancing the composition of word embeddings across heterogenous data sets
Stephanie Brandl, David Lassner, Maximilian Alber
Word embeddings capture semantic relationships based on contextual information and are the basis for a wide variety of natural language processing applications. Notably these relat…