NEAT - Named Entities in Archaeological Texts: a Semantic Approach to Term Extraction and Classification

The lack of annotated datasets affects the development of Natural Language Processing applications and heavily impacts the access to textual data, in particular for specific domains and specific languages. In this paper, we propose a methodology to anno tate texts concerning domain-specific knowledge, to provide a reliable source of data for the task of Named Entity Recognition (NER) in the domain of archaeology for the Italian laguage. This method integrates syntactic and semantic information from sev eral structured sources to annotate entities’ mentions in unstructured texts. Furthermore, we make use of an ontology to label en tities with the specific type they refer to. By using a corpus made up of item descriptions from Europeana’s Archaeology Collection, we first test our proposed methodology on a mock dataset composed of 1,000 texts. After several steps of improve ments, we use the final process to create a complete dataset composed of 5,000 descriptions. The resulting dataset, Named Entities in Archaeological Texts has a total of 41,002 spans of texts annotated with their domain-specific entity classification according to the CIDOC Conceptual Reference Model.