Named entity processing over historical texts is more and more being used due to the massive documents and archives being stored in digital libraries. However, due to the poor annotated resources of historical nature, information extraction performances fall behind those on contemporary texts. In this paper, we introduce the development of the NewsEye resource, a multilingual dataset for named entity recognition and linking enriched with stances towards named entities. The dataset is comprised of diachronic historical newspaper material published between 1850 and 1950 in French, German, Finnish, and Swedish. Such historical resource is essential in the context of developing and evaluating named entity processing systems. It evenly allows enhancing the performances of existing approaches on historical documents which enables adequate and efficient semantic indexing of historical documents on digital cultural heritage collections.

In this paper, we describe a method to restore a flow of continuous documents. The flow is a collection of consecutive scanned pages without explicit separation marks between documents. Our method is based on contextual and layout descriptors meant to specify the relationship between each pair of consecutive pages. The relationships are represented using vectors of features with boolean values indicating the presence or the absence of descriptors on concerned pages. The segmentation task therefore consists in classifying such vectors into continuities or breaks. The continuity class indicates that pages belong to the same document while the break class ends the ongoing document and starts a new one. The experimental part is based on a large collection of real administrative documents.