3 papers
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.LG2020
Automatic Identification of Types of Alterations in Historical Manuscripts
David Lassner, Anne Baillot, Sergej Dogadov +2
Alterations in historical manuscripts such as letters represent a promising field of research. On the one hand, they help understand the construction of text. On the other hand, to…
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…