Crossmentions

Crossmentions are references to the same real-world entity that appear in multiple locations within a single text or across multiple texts. The practice involves identifying these mentions and linking them to signal that they refer to the same person, place, organization, or concept. Crossmentioning helps readers and systems track connections, establish continuity, and support downstream tasks such as search, summarization, and knowledge integration.

In natural language processing, cross-document co-reference resolution (CDCR) aims to determine which mentions in different documents refer to the same entity. CDCR supports building knowledge graphs, improving entity linking, and enabling cross-document analytics that reveal networks of actors, places, and events.

In information management, crossmentions aid data integration and provenance. By linking mentions across datasets, organizations can resolve ambiguities, merge records, and maintain consistent identifiers for entities across systems.

In literature and journalism, crossmentions help map character relationships, track attribution, and verify consistency across chapters or articles. They are used to trace how people, places, and concepts recur and relate to one another over time.

Techniques range from manual annotation and rule-based heuristics to statistical machine learning and neural models. Challenges include name variants, abbreviations, aliases, polysemy, and privacy considerations when handling sensitive personal data.

See also cross-reference, coreference, entity resolution, cross-document co-reference.