Petitions are a rich historical source, yet they have been relatively little used in historical research. In this paper, we aim to analyse Swedish texts from around the 18th century, and petitions in particular, using automatic means of text classification. We also test how text pre-processing and different feature representations affect the result, and we examine feature importance for our main class of interest - petitions. Our experiments show that the statistical algorithms NB, RF, SVM, and kNN are indeed very able to classify different genres of historical text. Further, we find that normalisation has a positive impact on classification, and that content words are particularly informative for the traditional models. A fine-tuned BERT model, fed with normalised data, outperforms all other classification experiments with a macro average F1 score at 98.8. However, using less computationally expensive methods, including feature representation with word2vec, fastText embeddings or even TF-IDF values, with a SVM classifier also show good results for both unnormalise and normalised data. In the feature importance analysis, where we obtain the features most decisive for the classification models, we find highly relevant characteristics of the petitions, namely words expressing signs of someone inferior addressing someone superior.

In historical encrypted sources we can find encrypted text sequences, also called ciphertext, as well as non-encrypted cleartexts written in a known language. While most of the cryptanalysis focuses on the decryption of ciphertext, cleartext is often overlooked although it can give us important clues about the historical interpretation and contextualisation of the manuscript. In this paper, we investigate to what extent we can automatically distinguish cleartext from ciphertext in historical ciphers and to what extent we are able to identify its language. The problem is challenging as cleartext sequences in ciphers are often short, up to a few words, in different languages due to historical code-switching. To identify the sequences and the language(s), we chose a rule-based approach and run 7 different models using historical language models on various ciphertexts.

As the number of digitized archival documents increases very rapidly, named entity recognition (NER) in historical documents has become very important for information extraction and data mining. For this task an annotated corpus is needed, which has up to now been missing for Czech. In this paper we present a new annotated data collection for historical NER, composed of Czech historical newspapers. This corpus is freely available for research purposes. For this corpus, we have defined relevant domain-specific named entity types and created an annotation manual for corpus labelling. We further conducted some experiments on this corpus using recurrent neural networks. We experimented with randomly initialized embeddings and static and dynamic fastText word embeddings. We achieved 0.73 F1 score with a bidirectional LSTM model using static fastText embeddings.

Historical cryptology is the study of historical encrypted messages aiming at their decryption by analyzing the mathematical, linguistic and other coding patterns and their historical context. In libraries and archives we can find quite a lot of ciphers, as well as keys describing the method used to transform the plaintext message into a ciphertext. In this paper, we present work on automatically mapping keys to ciphers to reconstruct the original plaintext message, and use language models generated from historical texts to guess the underlying plaintext language.

In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different levels, have different attention mechanisms, and different neural network architectures. Our results show that NMT models are much better than SMT models in terms of character error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical spelling normalization. Transformer models perform better only when provided with more training data. We also find that subword-level models with a small subword vocabulary are better than character-level models. In addition, we propose a hybrid method which further improves the performance of historical spelling normalization.

In this paper, we describe our work on building a parallel treebank for a less studied and typologically dissimilar language pair, namely Swedish and Turkish. The treebank is a balanced syntactically annotated corpus containing both fiction and technical documents. In total, it consists of approximately 160,000 tokens in Swedish and 145,000 in Turkish. The texts are linguistically annotated using different layers from part of speech tags and morphological features to dependency annotation. Each layer is automatically processed by using basic language resources for the involved languages. The sentences and words are aligned, and partly manually corrected. We create the treebank by reusing and adjusting existing tools for the automatic annotation, alignment, and their correction and visualization. The treebank was developed within the project supporting research environment for minor languages aiming at to create representative language resources for language pairs dissimilar in language structure. Therefore, efforts are put on developing a general method for formatting and annotation procedure, as well as using tools that can be applied to other language pairs easily.