Analyzing and Improving the Quality of a Historical News Collection using Language Technology and Statistical Machine Learning Methods

Abstract

In this paper, we study how to analyze and improve the quality of a large historical newspaper collection. The National Library of Finland has digitized millions of newspaper pages. The quality of the outcome of the OCR process is limited especially with regard to the oldest parts of the collection. Approaches such as crowd-sourcing has been used in this field to improve the quality of the texts, but in this case the volume of the materials makes it impossible to edit manually any substantial proportion of the texts. Therefore, we experiment with quality evaluation and improvement methods based on corpus statistics, language technology and machine learning in order to find ways to automate analysis and improvement process. The final objective is to reach a clear reduction in the human effort needed in the post-processing of the texts. We present quantitative evaluations of the current quality of the corpus, describe challenges related to texts written in a morphologically complex language, and describe two different approaches to achieve quality improvements.