coreferenceaware

Coreferenceaware is an adjective used in natural language processing to describe data, models, or systems that explicitly handle coreference relations—how multiple expressions refer to the same entity across a discourse. By tracking entities through pronouns, proper names, and noun phrases, coreference awareness helps maintain a consistent representation of discourse entities.

In practice, coreference-aware systems integrate coreference resolution techniques—identifying mentions, linking them to antecedents, and maintaining a map of entities across text. Methods range from rule-based to statistical and neural approaches, and many modern architectures perform coreference as a joint or auxiliary task alongside named entity recognition, relation extraction, or summarization. The goal is to enable consistent entity grounding across sentences and paragraphs.

Applications include information extraction, question answering, document summarization, dialogue systems, and machine translation, where resolving who is being referred to prevents pronoun ambiguity and improves coherence and accuracy.

Challenges include long-range dependencies, pronoun and ellipsis resolution, domain shifts, and multilingual coreference. Evaluation typically uses established metrics from coreference datasets (for example, MUC, B3, CEAF, and LEA) to measure precision and recall of correctly linked mentions. Data quality and annotation guidelines significantly influence results.

See also coreference resolution, natural language processing, discourse analysis.