Named entity recognition applied on a data base of Medieval Latin charters. The case of chartae burgundiae

International audienceThe work on the named entity recognition (NER) in databases of historical texts has been placed among the most promising new ways to implement best recovery and managements tools for exploring mass data. In this paper, we describe the application processing NER through a modelling with CRF on an annotated database of Burgundy collection of charters from the tenth to thirteenth centuries. The aim is to generate a model for automatic recognition of named entities in historical sources. We discuss the nature of historical documents in the corpus and extraction of rules, and we expose adaptation to the processing algorithm and the most common problems encountered in Medio Latin texts using diplomatic formularies, which is an atypical case within the NER studies

Named entity recognition applied on a data base of Medieval Latin charters. The case of chartae burgundiae

Abstract The work on the named entity recognition (NER) in databases of historical texts has been placed among the most promising new ways to implement best recovery and managements tools for exploring mass data. In this paper, we describe the application processing NER through a modelling with CRF on an annotated database of Burgundy collection of charters from the tenth to thirteenth centuries. The aim is to generate a model for automatic recognition of named entities in historical sources. We discuss the nature of historical documents in the corpus and extraction of rules, and we expose adaptation to the processing algorithm and the most common problems encountered in Medio Latin texts using diplomatic formularies, which is an atypical case within the NER studies

Machine Learning Algorithm for the Scansion of Old Saxon Poetry

Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses