Domain-Specific Word Embeddings with Structure Prediction. (arXiv:2210.04962v1 [cs.CL])

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 methods do not offer a way to use or predict
information on structure between sub-corpora, time or domain and dynamic
embeddings can only be compared after post-alignment. We propose novel word
embedding methods that provide general word representations for the whole
corpus, domain-specific representations for each sub-corpus, sub-corpus
structure, and embedding alignment simultaneously. We present …

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