Takao Fujikawa’s research while affiliated with Osaka University and other places

Large text corpora are indispensable for natural language processing. However, in various fields such as literature and humanities, many documents to be studied are only scanned to images, but not converted to text data. Optical character recognition (OCR) is a technology to convert scanned document images into text data. However, OCR often misrecognizes characters due to the low quality of the scanned document images, which is a crucial factor that degrades the quality of constructed text corpora. This paper works on corpus construction for historical newspapers. We present a corpus construction method based on a pipeline of image processing, OCR, and filtering. To improve the quality, we further propose to integrate OCR error correction. To this end, we manually construct an OCR error correction dataset in the historical newspaper domain, propose methods to improve a neural OCR correction model and compare various OCR error correction models. We evaluate our corpus construction method on the accuracy of extracting articles of a specific topic to construct a historical newspaper corpus. As a result, our method improves the article extraction F score by $1.7\%$ via OCR error correction comparing to previous work. This verifies the effectiveness of OCR error correction for corpus construction.

Public meeting articles are the key to understanding the history of public opinion and public sphere in Australia. Information extraction from public meeting articles can obtain new insights into Australian history. In this paper, we create an information extraction dataset in the public meeting domain. We manually annotate the date and time, place, purpose, people who requested the meeting, people who convened the meeting, and people who were convened of 1258 public meeting articles. We further present an information extraction system, which formulates information extraction from public meeting articles as a machine reading comprehension task. Experiments indicate that our system can achieve an F1 score of 74.98% for information extraction from public meeting articles.