Transfer Learning across Several Centuries: Machine and Historian
  Integrated Method to Decipher Royal Secretary's Diary
Sojung Lucia Kim Taehong Jang Joonmo Ahn Hyungil Lee Jaehyuk Lee
A named entity recognition and classification plays the first and foremost
important role in capturing semantics in data and anchoring in translation as
well as downstream study for history. However, NER in historical text has faced
challenges such as scarcity of annotated corpus, multilanguage variety, various
noise, and different convention far different from the contemporary language
model. This paper introduces Korean historical corpus (Diary of Royal secretary
which is named SeungJeongWon) recorded over several centuries and recently
added with named entity information as well as phrase markers which historians
carefully annotated. We fined-tuned the language model on history corpus,
conducted extensive comparative experiments using our language model and
pretrained muti-language models. We set up the hypothesis of combination of
time and annotation information and tested it based on statistical t test. Our
finding shows that phrase markers clearly improve the performance of NER model
in predicting unseen entity in documents written far different time period. It
also shows that each of phrase marker and corpus-specific trained model does
not improve the performance. We discuss the future research directions and
practical strategies to decipher the history document.