Year: 2018
Type: conference-paper
Abstract: The lack of a spelling convention in historical documents makes their orthography to change depending on the author and the time period in which each document was written. This represents a problem for the preservation of the cultural heritage, which strives to create a digital text version of a historical document. With the aim of solving this problem, we propose three approaches—based on statistical, neural and character-based machine translation— to adapt the document’s spelling to modern standards. We tested these approaches in different scenarios, obtaining very encouraging results.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G4835283752, G5450370416
OpenAlex ID: W2807629868