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Found 4 papers, 1 papers with code
Domain-Specific Word Embeddings with Structure Prediction
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.
Automatic Identification of Types of Alterations in Historical Manuscripts
In addition to the findings based on the digital scholarly edition Berlin Intellectuals, we present a general framework for the analysis of text genesis that can be used in the context of other digital resources representing document variants.
Balancing the composition of word embeddings across heterogenous data sets
Word embeddings capture semantic relationships based on contextual information and are the basis for a wide variety of natural language processing applications.
Times Are Changing: Investigating the Pace of Language Change in Diachronic Word Embeddings
We propose Word Embedding Networks, a novel method that is able to learn word embeddings of individual data slices while simultaneously aligning and ordering them without feeding temporal information a priori to the model.
