NDLI: Analysis of Part-Of-Speech Tagging of Historical German Texts

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Analysis of Part-Of-Speech Tagging of Historical German Texts

Content Provider	ACM Digital Library
Author	Büchler, Marco Steding, David Weß, Maximilian Moritz, Maria Rotari, Gabriela Paluch, Markus
Abstract	The amount of data in contemporary digital corpora is too large to be processed manually, which increases the necessity for computer linguistic tools in humanities. However, the processing of natural languages is a challenge for automatic tools, because languages are used heterogeneously. To process a text, often taggers are used that are trained on a standardized language variety (e.g. recent newspaper articles). Unfortunately, these training data often differ from the target texts (i.e. the text on which a trained model later is applied) in terms of language variety and register, which is especially the case for historical texts. Therefore, additional, manual analyses are usually inevitable. Training tools on the target language variety, however, can improve the results of these tools so that the manual prost-processing could be avoided. Thus, the need to process large datasets of diachronic texts and to obtain accurate results in a short time-span requires an adaptable approach. The present paper suggests this adaptable approach, by training taggers on a target language variety, to improve the accuracy of the structure of historical German corpora at the level of part-of-speech-tagging (hereafter POS-tagging). We trained four taggers (Perceptron tagger [26], Hidden Markov Model (HMM) [1], Conditional Random Fields (CRF) [13], and Unigram [21]) each on data from three different literary periods: Baroque (1600-1700), Romanticism (1790-1840) and Modernism (1880-1930). Compared with pre-tagged data, we obtained a maximum accuracy in POS-tagging of 98.3% for a single period (Modernism with Perceptron trained on Modernism) and a maximum mean accuracy for all three periods of 94.3% (Perceptron trained on Romanticism). Compared with manually tagged data, we obtained a maximum accuracy for one period of 96.8% (Romanticism with CRF and HMM trained on Romanticism) and a maximum mean accuracy for all three periods of 92.3% (Perceptron trained on Romanticism). In spite of the heterogeneity of literary data, these results demonstrate a high performance of the POS-taggers if the models are trained on target language varieties. Therefore, this adaptable approach provides reliable data allowing the use of taggers for analysis of different historical texts.
Starting Page	41
Ending Page	46
Page Count	6
File Format	PDF
ISBN	9781450352659
DOI	10.1145/3078081.3078111
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Diachronic texts Natural language processing Historical german data Reliability
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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