After decades of massive digitisation, an unprecedented number of historical documents are
available in digital format, along with their machine-readable texts. While this represents a …

This paper presents an overview of the second edition of HIPE (Identifying Historical People,
Places and other Entities), a shared task on named entity recognition and linking in …

After decades of massive digitisation, an unprecedented amount of historical documents is
available in digital format, along with their machine-readable texts. While this represents a …

Large language models (LLMs) have been leveraged for several years now, obtaining state-
of-the-art performance in recognizing entities from modern documents. For the last few …

Traditional NER systems are typically trained to recognize coarse-grained entities, and less
attention is given to classifying entities into a hierarchy of fine-grained lower-level subtypes …

Compared to standard Named Entity Recognition (NER), identifying persons, locations, and
organizations in historical texts constitutes a big challenge. To obtain machine-readable …

This paper describes NEREL—a Russian news dataset suited for three tasks: nested named
entity recognition, relation extraction, and entity linking. Compared to flat entities, nested …

This paper presents an overview of the second edition of HIPE (Identifying Historical People,
Places and other Entities), a shared task on named entity recognition and linking in …

Tracking characters and locations throughout a story can help improve the understanding of
its plot structure. Prior research has analyzed characters and locations from text …

The efficacy of large-scale language models (LLMs) as few-shot learners has dominated the
field of natural language processing, achieving state-of-the-art performance in most tasks …