Abstract
Digi collection contains 1,960,921 pages of newspaper material from years 1771–1910 both in Finnish and Swedish. We use only material of Finnish documents in our evaluation. The OCRed newspaper collection has lots of OCR errors; its estimated word level correctness is about 70–75% [7]. Our baseline NER tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. Three other available tools are also evaluated: a Finnish Semantic Tagger (FST), Connexor’s NER tool and Polyglot’s NER.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2nd International Conference on Digital Access to Textual Cultural Heritage |
| Number of pages | 6 |
| Place of Publication | New York |
| Publisher | ACM |
| Publication date | 1 Jun 2017 |
| Pages | 181-186 |
| ISBN (Electronic) | 978-1-4503-5265-9 |
| DOIs | |
| Publication status | Published - 1 Jun 2017 |
| MoE publication type | A4 Article in conference proceedings |
| Event | DaTeCH 2017 - Göttingen, Germany Duration: 1 Jun 2017 → 2 Jun 2017 |
Fields of Science
- 113 Computer and information sciences
Cite this
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Names, Right or Wrong : Named Entities in an OCRed Historical Finnish Newspaper. / Kettunen, Kimmo Tapio; Ruokolainen, Teemu Petteri.
Proceedings of the 2nd International Conference on Digital Access to Textual Cultural Heritage. New York : ACM, 2017. p. 181-186.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
TY - GEN
T1 - Names, Right or Wrong
T2 - Named Entities in an OCRed Historical Finnish Newspaper
AU - Kettunen, Kimmo Tapio
AU - Ruokolainen, Teemu Petteri
N1 - Volume: Proceeding volume:
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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. In general a NER system’s performance is genre and domain dependent and also used entity categories vary [16]. The most general set of named entities is usually some version of three partite categorization of locations, persons and organizations. In this paper we report evaluation result of NER with data out of a digitized Finnish historical newspaper collection Digi. Experiments, results and discussion of this research serve development of the Web collection of historical Finnish newspapersDigi collection contains 1,960,921 pages of newspaper material from years 1771–1910 both in Finnish and Swedish. We use only material of Finnish documents in our evaluation. The OCRed newspaper collection has lots of OCR errors; its estimated word level correctness is about 70–75% [7]. Our baseline NER tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. Three other available tools are also evaluated: a Finnish Semantic Tagger (FST), Connexor’s NER tool and Polyglot’s NER.
AB - 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. In general a NER system’s performance is genre and domain dependent and also used entity categories vary [16]. The most general set of named entities is usually some version of three partite categorization of locations, persons and organizations. In this paper we report evaluation result of NER with data out of a digitized Finnish historical newspaper collection Digi. Experiments, results and discussion of this research serve development of the Web collection of historical Finnish newspapersDigi collection contains 1,960,921 pages of newspaper material from years 1771–1910 both in Finnish and Swedish. We use only material of Finnish documents in our evaluation. The OCRed newspaper collection has lots of OCR errors; its estimated word level correctness is about 70–75% [7]. Our baseline NER tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. Three other available tools are also evaluated: a Finnish Semantic Tagger (FST), Connexor’s NER tool and Polyglot’s NER.
KW - 113 Computer and information sciences
KW - named entity recognition
U2 - 10.1145/3078081.3078084
DO - 10.1145/3078081.3078084
M3 - Conference contribution
SP - 181
EP - 186
BT - Proceedings of the 2nd International Conference on Digital Access to Textual Cultural Heritage
PB - ACM
CY - New York
ER -