AnaphoraAuflösung

AnaphoraAuflösung, commonly known as anaphora resolution or coreference resolution, is the task of identifying the antecedents of anaphoric expressions in text or discourse. Anaphors include pronouns, definite noun phrases, and zero pronouns in pro-drop languages; the antecedent is the linguistic object to which the anaphor refers, often across sentence boundaries. Effective anaphoraAuflösung is essential for accurate interpretation of documents and dialogues.

Approaches range from rule-based systems that apply linguistic constraints (such as Binding Theory) and salience heuristics to statistical and machine-learning methods that score candidate antecedents using features such as distance, syntactic role, gender and number agreement, named-entity compatibility, and semantic similarity. In recent years, neural end-to-end models have dominated the field, learning representations for mentions and performing document-level clustering of referents with transformer architectures.

Evaluation typically uses annotated corpora and standard metrics such as MUC, B3, CEAF, and overall CoNLL F1. The most common datasets include OntoNotes-based corpora and various language-specific resources. Challenges include long-range dependencies, ambiguous or implicit antecedents, cross-sentential resolution, and languages with rich pro-drop phenomena or free word order. Biases in training data can also affect performance.

Applications of AnaphoraAuflösung include improving information extraction, question answering, summarization, machine translation, and dialogue systems. Ongoing research addresses multilingual coreference, event and discourse-level resolution, and integrating world knowledge to disambiguate references.