Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language

Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language, Gothenburg, Sweden, May 22, 2017

Normalizing Medieval German Texts: from rules to deep learning

The application of NLP tools to historical texts is complicated by a high level of spelling variation. Different methods of historical text normalization have been proposed. In this comparative evaluation I test the following three approaches to text canonicalization on historical German texts from 15th–16th centuries: rule-based, statistical machine translation, and neural machine translation....

Comparing Rule-based and SMT-based Spelling Normalisation for English Historical Texts

To be able to use existing natural language processing tools for analysing historical text, an important preprocessing step is spelling normalisation, converting the original spelling to present-day spelling, before applying tools such as taggers and parsers. In this paper, we compare a probablistic, language-independent approach to spelling normalisation based on statistical machine translatio...

OCR and post-correction of historical Finnish texts

This paper presents experiments on Optical character recognition (OCR) as a combination of Ocropy software and data-driven spelling correction that uses Weighted Finite-State Methods. Both model training and testing were done on Finnish corpora of historical newspaper text and the best combination of OCR and post-processing models give 95.21% character recognition accuracy.

HistoBankVis: Detecting Language Change via Data Visualization

We present HistoBankVis, a novel visualization system designed for the interactive analysis of complex, multidimensional data to facilitate historical linguistic work. In this paper, we illustrate the visualization’s efficacy and power by means of a concrete case study investigating the diachronic interaction of word order and subject case in Icelandic.