This paper presents an overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and English. Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of deep learning architectures and the consideration of textual material originating from historical and cultural heritage collections. While the former opens up new opportunities, the latter introduces new challenges with heterogeneous, historical and noisy inputs. In this context, the objective of HIPE, run as part of the CLEF 2020 conference, is threefold: strengthening the robustness of existing approaches on non-standard inputs, enabling performance comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents. Tasks, corpora, and results of 13 participating teams are presented.

Abstract. This paper describes a work-flow designed to populate a digital library of ancient Greek critical editions with highly accurate OCR scanned text. While the most recently available OCR engines are now able after suitable training to deal with the polytonic Greek fonts used in 19th and 20th century editions, further improvements can also be achieved with postprocessing. In particular, the progressive multiple alignment method applied to different OCR outputs based on the same images is discussed in this paper.

Even large citation indexes such as the Web of Science, Scopus or Google Scholar cover only a small fraction of the literature in the humanities. This coverage sensibly decreases going backwards in time. Citation mining of humanities publications — defined as an instance of bibliometric data mining and as a means to the end of building comprehensive citation indexes — remains an open problem. In this contribution we discuss the results of two recent projects in this area: Cited Loci and Linked Books. The former focused on the domain of classics, using journal articles in JSTOR as a corpus; the latter considered the historiography on Venice and a novel corpus of journals and monographs. Both projects attempted to mine citations of all kinds — abbreviated and not, to all types of sources, including primary sources — and considered a wide time span (19th to 21st century). We first discuss the current state of research in citation mining of humanities publications. We then present the various steps involved into this process, from corpus selection to data publication, discussing the peculiarities of the humanities. The approaches taken by the two projects are compared, allowing us to highlight disciplinary differences and commonalities, as well as shared challenges between historiography and classics on this respect. The resulting picture portrays humanities citation mining as a field with a great, yet mostly untapped potential, and a few still open challenges. The potential lies in using citations as a means to interconnect digitized collections at a large scale, by making explicit the linking function of bibliographic citations. As for the open challenges, a key issue is the existing need for an integrated metadata infrastructure and an appropriate legal framework to facilitate citation mining in the humanities.

A variety of schemas and ontologies are currently used for the machine-readable description of bibliographic entities and citations. This diversity, and the reuse of the same ontology terms with different nuances, generates inconsistencies in data. Adoption of a single data model would facilitate data integration tasks regardless of the data supplier or context application. In this paper we present the OpenCitations Data Model (OCDM), a generic data model for describing bibliographic entities and citations, developed using Semantic Web technologies. We also evaluate the effective reusability of OCDM according to ontology evaluation practices, mention existing users of OCDM, and discuss the use and impact of OCDM in the wider open science community.

Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of deep learning architectures and the consideration of textual material originating from historical and cultural heritage collections. While the former opens up new opportunities, the latter introduces new challenges with heterogeneous, historical and noisy inputs. If NE processing tools are increasingly being used in the context of historical documents, performance values are below the ones on contemporary data and are hardly comparable. In this context, this paper introduces the CLEF 2020 Evaluation Lab HIPE (Identifying Historical People, Places and other Entities) on named entity recognition and linking on diachronic historical newspaper material in French, German and English. Our objective is threefold: strengthening the robustness of existing approaches on non-standard inputs, enabling performance comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents in order to support scholarship on digital cultural heritage collections.