Name the Name

Abstract

Named Entity Recognition (NER), search, classification, and tagging of names and name like frequent informational elements in texts, has become a standard information extraction procedure for textual data. NER has been applied to many types of texts and different types of entities: newspapers, fiction, historical records, persons, locations, chemical compounds, protein families, animals etc. Performance of a NER system is usually quite heavily genre and domain dependent. Entity categories used in NER may also vary. The most used set of named entity categories is usually some version of three partite categorization of locations, persons, and organizations. In this paper we report evaluation results with data extracted from a digitized Finnish historical newspaper collection Digi using two statistical NER systems, namely, Stanford Named Entity Recognizer and LSTM-CRF NER model. The OCRed newspaper collection has lots of OCR errors; its estimated word level correctness is about 70–75%. Our NER evaluation collection and training data are based on ca. 500 000 words which have been manually corrected from OCR output of ABBYY FineReader 11. We have also available evaluation data of new uncorrected OCR output of Tesseract 3.04.01. Our Stanford NER results are mostly satisfactory. With our ground truth data we achieve F-score of 0.89 with locations and 0.84 with persons. With organizations the result is 0.60. With re-OCRed Tesseract output the results are 0.79, 0.72, and 0.42, respectively. Results of LSTM-CRF are similar.