Coreference resolution
Coreference resolution is the task of identifying expressions in text that refer to the same entity and linking those expressions to it.
Natural Language Processing in Action, Second Edition
Coreference resolution is the task of determining which entity or expression a referring expression, such as a pronoun, denotes in text.
- How it is used
- Connects repeated mentions in a knowledge-extraction pipeline so knowledge graphs avoid duplicate entities and potentially incorrect relations.
- How deep it goes
- Defines the task, examines difficult pronouns, and demonstrates Coreferee chains and setup without explaining resolver internals.
- Examples
- Uses Maria’s pronouns, a Timnit Gebru article and Coreferee chains, and an ambiguous city-councilmen sentence.
- What you can do after
- Apply it via Coreferee in a spaCy pipeline and inspect chains of mentions before consolidating extracted entities.
Read how Natural Language Processing in Action, Second Edition explains coreference resolution →
Graph Algorithms for Data Science
Coreference resolution is the task of identifying expressions in text that refer to the same entity and linking them to that entity.
- How it is used
- Supports information extraction, text summarization, and question-answering workflows by resolving references before downstream processing.
- How deep it goes
- Explains definitions, ambiguity, approach types, and an implementation, but stops short of training models or detailed optimization.
- Examples
- Uses Google and YouTube ambiguity, plural company references, and an Apple Inc. passage with deliberately exposed resolution errors.
- What you can do after
- Apply it via SpaCy and coreferee to replace references with resolved entities and inspect downstream extraction risks.
Read how Graph Algorithms for Data Science explains coreference resolution →
Knowledge Graphs and LLMs in Action
Coreference resolution is the technique of determining which entity or expression a referring phrase, such as a pronoun or shortened name, denotes.
- How it is used
- Supports accurate text interpretation and knowledge-graph extraction by linking referring expressions to entities.
- How deep it goes
- Defines pronoun reference through an ambiguity example and notes shortened-name failures; stops short of specifying a resolution algorithm.
- Examples
- Uses the John–Bob pronoun ambiguity and archival abbreviations such as “S.” and “U.Cal.” as concrete cases.
- What you can do after
- Apply prompted GPT to resolve references and extract related entities and relations, while recognizing failures on shortened names.
Read how Knowledge Graphs and LLMs in Action explains coreference resolution →
Related concepts
- Corefereeimplemented by · Coreferee is an open-source spaCy extension for resolving coreferences in text.
- structured extractionpart of · Structured extraction is the process of identifying specified information in source content and representing it as organized data in a defined structure.
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